Computer Vision and Pattern Recognition 129
☆ GazeRIB: Systematic Evaluation of Multi-Reader Gaze-Guided Chest X-ray Learning
Nattakorn Kittisut, Roy M. Gabriel, Marly van Assen, Mohammadreza Zandehshahvar, Carlo N. De Cecco, Elizabeth A. Krupinski, Ali Adibi
Radiologists' visual attention can supervise medical image learning, but how reader panel composition affects its value remains untested. We evaluated GazeRIB (Gaze-guided Radiologist-Informed Blackout), which masks unviewed regions during training and uses unmasked images at inference. Eight experts repeatedly read the same 80 chest X-rays (CXRs). We compared individual readers with three-, five- and eight-reader panels across seven architectures and 10 seeds, evaluating 910 models on a separate 2,897-CXR cohort for four-class severity prediction. Every panel improved macro F1 over the no-gaze baseline by 5.33-7.47 percentage points (pp; Bonferroni-adjusted p < 0.007); no individual reader reached significance on this endpoint. Three readers captured 73-80% of the eight-reader gain in macro F1, the area under the receiver-operating-characteristic curve (ROC-AUC) and the area under the precision-recall curve (AU-PRC). On external validation without retraining, all four panels improved ROC-AUC on SIIM-FISABIO-RSNA by +1.93 to +4.24 pp (p <= 0.004); on MIMIC-Eye no panel differed significantly from baseline, whereas the single-reader average declined (-1.40 pp, p = 0.008). Reader composition therefore influenced performance, although transfer depended on the cohort.
comment: 20 pages on main manuscripts (3 tables + 4 figures), 14 pages supplementary (10 tables + 8 figures)
☆ Latent Reasoning in Continuous Space for Unified Multimodal Models NeurIPS 2026
Unified Multimodal Models (UMMs) achieve remarkable success on diverse multimodal understanding and generation tasks, yet they still struggle to effectively leverage language-based chain-of-thought reasoning for image generation. As a result, existing reasoning-augmented UMMs often fail to inherit the test-time scaling and fine-grained controllability observed in large language models. We attribute this failure to a fundamental modality mismatch: reasoning is performed in a discrete token space, whereas image generation operates in a continuous space. To bridge this gap, we introduce a Latent Reasoning framework in Continuous space (LARC), which alleviates the token-level bottleneck by reasoning over continuous hidden states. Specifically, the training framework consists of two stages: (i) curriculum supervised fine-tuning (SFT), and (ii) information-gain reinforcement learning (RL). First, the curriculum SFT stage gradually converts token-level reasoning into latent reasoning. Subsequently, the information-gain RL stage self-evolves latent reasoning without ground truth supervision. Experiments across diverse text-to-image generation benchmarks show that LARC consistently improves generation quality, prompt following, and compositional fidelity. Notably, LARC achieves state-of-the-art performance on GenEval with a 7%p gain over BAGEL and demonstrates clear test-time scaling behavior, outperforming token-based generation under both parallel and sequential test-time scaling strategies.
comment: Published as conference proceeding for NeurIPS 2026. Last two authors advised equally
☆ SemEllipsoid: Semantic-aware Shape Abstraction with Ellipsoid
Representing 3D shapes using a set of primitives - known as shape abstraction - is an actively studied problem in computer graphics with wide applications in shape understanding, analysis, and simulation due to the compactness and computational efficiency of the primitive-based representation. Prior work has primarily focused on geometry-aware shape abstraction, where the objective is to approximate the input shape with a minimal set of primitives while preserving geometric fidelity. However, these methods often overlook semantic consistency, inevitably leading to primitives that span across multiple semantic parts. Such inconsistency can cause undesirable artifacts in downstream tasks such as part-level shape editing or manipulation. In this paper, we introduce a new task - semantic-aware shape abstraction, where the goal is to produce abstractions that are both geometrically faithful and semantically consistent, ensuring that each primitive adheres strictly to a single semantic part without crossing semantic boundaries. We propose SemEllipsoid, a novel method to address this problem using ellipsoidal primitives. The core idea is to inject semantic constraints into the primitive optimization process leveraging the availability of semantic cues from large-scale foundation models. We design a fully differentiable projection-blending-evaluation scheme to measure semantic divergence against input semantic masks, and formulate an alternating optimization strategy to iteratively solve for optimal semantic assignment and primitive configurations. Our method is non-learning-based, requiring no data collection or network training, and generalizes well to shapes from arbitrary categories. It also supports different levels of abstraction according to user demand. Extensive experiments and evaluations validate the effectiveness of our approach across several 3D shape collections.
☆ RoboArchitect: Automating the Design of Robotic Agents with Persistent Memory
Recent work shows that a frozen robot policy performs far better inside an agent harness, where control code decides when to query the policy, consult vision tools, or command the arm directly. Yet today these harnesses are built by hand and redesigned for each new robot or benchmark. We show that harness design can be automated, offering a strong alternative to finetuning on a novel setup. In RoboArchitect, a coding model observes a robot's rollouts, diagnoses failures, and iteratively revises the harness around the frozen policy. The harness evolves on a set of episodes for its optimization and is evaluated on held-out scenes and tasks. Without updating any model weights, RoboArchitect raises average success from 9.7% to 43.3% on unseen scenes (4.5x) and from 12.7% to 40.2% on unseen tasks (3.2x), across multiple benchmarks and policies. The gains are largest where the benchmark is far from the policy's training data: wrapping a general-purpose policy, the evolved harness yields a specialist that often outperforms even the finetuned version. Intuitive control strategies are automatically designed, such as lifting a jammed arm clear or reaching in horizontally when an obstacle blocks the way from above. The harness also evolves its own persistent memory, carrying learned experience, such as effective grasp depths, into later decisions. These results suggest that substantial headroom in robotic manipulation lies in the agent harness, and its design can be effectively automated and optimized without weight updates.
comment: Project Website: https://roboarchitect-ai.github.io/
☆ ReLift: Dense 3D Tracking as Lifted Trajectory Repair
Dense 3D tracking from monocular video requires establishing point correspondences over time, estimating their depth, and inferring their trajectories during occlusion. Existing methods typically learn these tasks jointly, a demanding objective given the scarcity of 4D supervision. We show that strong pretrained 2D trackers and 3D geometry models provide an effective foundation: lifted trajectories, with occluded segments simply interpolated, already rival dedicated 3D trackers using the same geometry. However, lifted trajectories inherit correspondence and depth errors, and under occlusion, their depth comes from the occluder rather than the tracked point. We introduce ReLift, an efficient feed-forward framework that recasts dense 3D tracking as geometric trajectory repair and completion. At its core, a trajectory corrector alternates attention along and across trajectories to preserve reliable observations, repair corrupted positions, and complete missing motion. Using only geometric inputs allows us to train directly on 3D trajectories with simulated lifting errors, decoupling training from images and from any specific tracker or geometry model. Concentrating 4D supervision on trajectory refinement yields a reusable model that consistently improves lifted trajectories across trackers and geometry providers without retraining, so it benefits directly from future advances in either component. On WorldTrack, ReLift achieves state-of-the-art mean accuracy, improving on the strongest baselines by 2.1 APD on all points and 3.0 on dynamic points, with faster configurations remaining competitive with leading 3D trackers while running faster than 4RC. Visit our supplementry page for more examples and code.
☆ A Better Critic Matters for Distribution Matching Distillation
Zongze Wu, Xianbang Wang, Zhengyang Geng, Mingdeng Cao, Zhoutong Zhang, Richard Zhang, Eli Shechtman, Ruiming Cao
Distribution Matching Distillation (DMD) trains a few-step student using the difference between a teacher's score and an online critic's estimate of the student distribution's score. We investigate what the critic needs, in capacity and in information, to provide a reliable training signal. On the capacity side, controlled experiments across seven SiT model sizes on ImageNet show that increasing teacher size while keeping a small critic fixed can degrade student quality and cause training collapse. Scaling the critic alongside the teacher, a strategy we call joint scaling, prevents collapse in our experiments and allows the student to benefit from larger teachers. On the information side, rather than forcing the critic to model a step-agnostic mixture of student outputs, we introduce a step-aware critic (STAC) that conditions on the student's generation step to estimate step-specific scores. Together, joint scaling and STAC consistently improve few-step distillation across class-conditional ImageNet generation, text-to-image generation with SDXL and FLUX.2, and text-to-video generation with Wan2.1.
comment: 35 pages, 16 figures. Project page and code: https://peppaking8.github.io/critic-matters/
☆ Rethinking Asymmetry in Self-Supervised 3D Geometry Learning
Self-supervised learning relies on asymmetry between what a model observes and what it must explain. Recent methods learn 3D geometry from unposed images by predicting cameras and scene structure, then rendering held-out views. To avoid trivial solutions, they impose this asymmetry in the input space, restricting how views interact through separate branches or masked attention. We revisit this design with geometric accuracy, rather than rendering quality, as the main objective. When a held-out view lies between context views, the context already constrains the geometry it observes. We therefore impose the asymmetry in the 3D representation space, excluding held-out views only from the reconstructed scene while a shared backbone uses all views for correspondence. This improves depth and pose estimation over input-space designs. We further exploit view density: sparse and dense observations depict the same scene but provide different amounts of correspondence. Aligning sparse-context features with dense-context features supplies a geometric training signal alongside rendering, without a pretrained geometric teacher. We additionally align features with a frozen vision foundation model, which brings further gains. None of these signals requires geometric annotation, and the resulting model improves geometry estimation over prior self-supervised methods. Controlled experiments isolate the contribution of each component, and self-supervised pretraining also improves subsequent supervised training.
comment: Website: https://gynjn.github.io/rethinking-asymmetry/
☆ Think in 360°: Benchmarking and Bootstrapping Panoramic Video Reasoning
A panoramic video captures all directions around the camera, but relevant evidence can appear in different directions and at different times. Understanding it requires models to connect observations across the viewing sphere and time. We introduce PanoVideoBench, the first manually annotated benchmark for comprehensive panoramic video understanding. Its 1,000 questions span five categories and 15 question types, with diagnostic annotations for target search and panoramic capabilities. Evaluation of 11 proprietary, open-source, and panoramic-specialized models identifies cross-view tracking and sphere-time evidence integration as their weakest capabilities. To strengthen these capabilities, we construct PanoVideoCoT, the first reasoning dataset grounded in real panoramic video, through sphere-time-grounded synthesis that converts verified panoramic observations and successful agentic explorations into 13,465 QA-CoT pairs. Starting from a general video MLLM, PanoVideoReasoner learns from this corpus through Panoramic CoT SFT and Evidence-Privileged On-Policy Self-Distillation, a panoramic instantiation of Vision-OPD's OPSD in which the teacher uses verified perspective views to guide student-generated trajectories. It reaches 55.7% on PanoVideoBench, improving the backbone by 12.5 points and the previous SOTA by 2.6. The gains concentrate on the same spatial, cross-view, and temporal capabilities exposed by the benchmark, showing that grounded sphere-time supervision can equip a general video MLLM with panoramic reasoning.
☆ REA: A Routed Embodied Agent Runtime for Asynchronous Brain-Cerebellum Control
Pengfei Zhou, Yixing Ma, Zhengfeiyang Zhang, Haoyuan Chen, Zhenglin Wan, Hexin Wang, Wangbo Zhao, Yang You
Foundation models are becoming remarkably reliable at high-level reasoning, yet they remain too slow and costly to serve as fine-grained controllers. Conversely, fast policies like VLAs and whole-body controllers offer more efficient reflexes, but struggle to generalize. While pairing a deliberative brain (foundation models) with a reactive cerebellum (fast policies) seems natural for embodied systems, two obstacles remain: methods are built and scored on incompatible stacks, and their asynchronous coordination is difficult. For example, an agent that calls the VLA in the agentic loop can stall the fast execution, while a VLA that calls the agent on errors tends to re-trigger fallbacks repeatedly or completely miss silent failures. To address this, we propose REA (Routed Embodied Agent), a unified runtime where a deliberative System 2, an embodied router System 1 and reactive System 0 policies cooperate under a single golden rule - no running policy waits on the brain. Instead of querying the brain at each failure, REA employs a route memory that stores each policy's measured success ahead of time, a boundary-triggered judge gate that consults the brain on policy execution, and a shared-budget escalation ladder for bounded recovery. Evaluated on 3,000 test episodes across 60 LIBERO-Pro tasks, REA reaches 95.9% success and beats the best single-agent baseline in its own policy pool (94.7%) while reducing control steps by 25% and control time by 17%. In a separate 100-episode RoboTwin 2.0 study, REA achieves 84% success with VLA policies and 86% with VLA+code, compared with 80% for the strongest single VLA baseline MEDex. These results demonstrate learned and programmatic policies operating within the same runtime. REA also transfers to BEHAVIOR-1K and a real Unitree G1 humanoid, achieving the highest observed success among the compared methods.
comment: 32 pages, 14 figures
☆ Learning Clean Generative Processes from Noisy Measurements
We study how to learn a generative model of clean images using only noisy observations. Diffusion processes and their generalizations to non-Gaussian noise require posterior predictions at every noise level. We first show that it is possible to learn these predictions from independent noisy views using Noise2Noise regression, but only at or above the noise level of the training data. To learn the predictions below that level, needed to generate clean images, we impose consistency: a prediction should equal its expectation after continuing the sampling process. We derive conditions under which consistency uniquely determines the continuation to the clean image distribution and verify them for Poisson, binomial, Gamma, and negative-binomial measurements. Experiments on CIFAR-10, photon-counting microscopy, and synthetic aperture radar show that the required posterior predictions can be learned from noisy views alone and that the consistency relation repairs the continuation beyond the noise level of the data, which severely deteriorates without consistency.
☆ Any Forcing: Any-Step Generality and One-Step Stability for Autoregressive Video Generation
Autoregressive video generation enables efficient inference through streaming prediction. However, existing methods are typically restricted to fixed sampling steps, limiting the flexibility to balance generation quality and efficiency, while one-step generation suffers from degraded visual quality and error accumulation over long sequences, leading to content drift and generation collapse. To address these issues, we propose Any Forcing, a unified framework for achieving Any-Step Generality and One-Step Stability. Specifically, we introduce a unified adversarial discriminator that jointly performs real-distribution alignment, inter-step consistency alignment, and first-frame quality enhancement, enabling flexible sampling while preserving one-step generation quality. Furthermore, we propose a training-free adaptive long-term memory selection mechanism that selects historical states based on global semantic relevance and local value contribution, while remapping their temporal positions to preserve temporal relationships within the memory cache, thereby enabling stable one-step long-video generation. Experiments demonstrate that Any Forcing achieves high-quality and stable one-step autoregressive video generation, achieving 20.9 FPS and 24.7 FPS for short and long video generation on a single H100 GPU, respectively.
comment: Autoregressive video generation
☆ GeoFlip: Benchmarking Geometry-Sensitive Physics in Video Generation
Video generation models synthesize realistic motion, but can they respond correctly to changes in initial geometry? We introduce GeoFlip, a video generation benchmark centered on physical outcome flips induced by geometry. With shared physical settings and textual prompts held fixed, GeoFlip applies controlled interventions to support extent, object placement, and mechanism configuration, constructing paired scenes with contrasting reference events, such as remaining stable versus falling or tipping. These pairs test whether models translate geometric differences in the first frame into correct physical evolution. The benchmark comprises 482 scene pairs and 964 reference videos across six physical categories, with reference outcomes established through procedural scene construction, physics simulation, and numerical validation and verification of physical events. We evaluate 16 models for image to video generation along three dimensions: event correctness, structural integrity, and similarity to reference videos. We examine evaluation consistency with independent vision language judges. On the 442 conditions shared by all models, the highest event correctness averaged across tasks is 27.98%, revealing substantial limitations in physical generation conditioned on geometry. Model strengths vary across physical mechanisms, and the three evaluation dimensions capture distinct capabilities. Typical failures include ignoring geometric changes, generating incorrect object motion, bending rigid objects, and introducing additional components. Rank correlations between judges reach 0.906 for event correctness and 0.929 for structural integrity. By combining controlled geometric interventions, verifiable physical references, and multidimensional evaluation, GeoFlip assesses progress from visual realism toward physical consistency conditioned on geometry.
comment: 14 pages, 7 figures
☆ MoETok: Sparse Expert Routing for 1D Visual Tokenizers
Jiahao Guo, Bao Tang, Haoxiang Cao, Jingfeng Yao, Bo Li, Wenyu Liu, Kun Gai, Changqian Yu, Xinggang Wang
Modern visual tokenizers aim to preserve fine-grained visual details while learning high-level semantic representations for downstream generation and understanding. Query-based 1D visual tokenizers encode images into flexible latent sequences, yet how individual learnable queries gather and encode image information remains poorly understood. We reveal that these queries spontaneously develop complementary roles under semantic supervision: a small set of Content queries captures high-level semantic information, while a larger set of Detail queries preserves fine-grained visual details for reconstruction. To adapt token processing to these heterogeneous roles, we introduce MoETok, a 1D visual tokenizer that routes tokens through sparsely activated experts. Jointly optimized with reconstruction and semantic objectives, the router learns role-specialized processing paths adapted to each token's representation. Across model scales and token budgets, MoETok consistently improves reconstruction, semantic representation, and autoregressive generation over dense tokenizers under matched supervision and active computation. On ImageNet, it combines strong reconstruction and semantic representation with state-of-the-art class-conditional generation within the 1D tokenizer family. We further validate MoETok at scale in a unified autoregressive model jointly trained for understanding and generation with a next-token cross-entropy objective. The resulting model combines competitive visual understanding with state-of-the-art generation among unified discrete models, matching or surpassing diffusion-based models such as BAGEL and Qwen-Image on benchmarks including DPG-Bench (87.61), GenEval2 (40.04 GM), and WISE (0.49).
comment: Project page: https://gjhhust.github.io/moetok-project
☆ FoAViS: Analyzing Learned Fixation Strategies in a Foveated Active Vision System
Human vision is foveated and active: resolution is highest at the fovea and falls off towards the periphery, and fixations are actively directed to locations of interest. Building a machine system that processes information in the same way lets us study gaze behavior in a controlled setting where the system's components and objectives are known. The emergence of gaze characteristics can therefore be examined systematically, which may in turn inform our understanding of biological vision. This requires a model that can sample like the fovea, handle various tasks from classification to dense prediction, and be trained concurrently with a fixation policy. In our study, we present such a system and study its capabilities and the fixation policies that emerge from it. Our system, FoAViS, samples visual input by adopting the established log-polar foveation and accumulates information in a 2D spatial memory. Due to its 2D structure, the same memory can be read out globally for classification or densely for semantic segmentation, without any architectural change. Fixations are drawn from a 2D map by a policy trained concurrently with the task. We find that learned fixation policies select more informative, task-specific locations than non-learned heuristics. These policies also shape the task representations in a beneficial way: for both classification and segmentation, models trained concurrently with a policy outperform models trained with random locations, even when the latter are provided with learned scanpaths. Moreover, these scanpaths exhibit similarities to human gaze behavior by capturing aspects of empirical scanpath characteristics. Our work marks an important step away from standard passive, single-pass architectures, towards biologically grounded models of vision. It opens a path for studying goal-directed gaze behavior and information representation in the visual system.
comment: 79 pages, 6 figures. Code: https://github.com/ecker-lab/FoAViS
☆ TwinPast: Benchmarking Historical Evidence Use in Streaming Video Understanding with Evidence Contrastive Video Pairs
High accuracy on streaming video benchmarks does not establish whether a model uses visual history. Our review of OVO-Bench and StreamingBench finds that recent frames suffice for 32% of sampled historical questions, while models also answer many questions correctly from the question and options alone. We introduce TwinPast to test whether answers correctly follow changes in history, using 350 human-reviewed video pairs spanning five historical tasks. Each pair holds the recent video context, question, and ordered options fixed while changing critical historical evidence and the correct answer. Each video is evaluated independently, and PairAcc requires correct answers for both histories. We evaluate 34 model and input configurations. For Qwen3-VL-8B and DeepSeek-Flash, historical inputs improve performance on TwinPast, reversing the recent-frame advantage on existing benchmarks. The strongest evaluated streaming system reaches 49.14% PairAcc, compared with 66.57-91.43% for the evaluated general-purpose models. This gap persists across all five tasks. Case studies further show that models select states from the wrong time or contradict recorded event order despite retaining the relevant evidence. These findings highlight the need to connect historical memory and retrieval with reasoning that uses evidence according to the question's temporal references and event relations.
☆ IGDA-ICMH: an Importance Guided Feature and Network Dual Adaptation Framework towards Image Compression for Machine and Human Vision IEEE
With the surge of machine vision applications, image compression for both machine and human vision (ICMH) has become an important research area. Existing studies mainly focus on task-specific adaptation or multi-task joint optimization, yet they often overlook the unequal contributions of different features and network layers to downstream machine vision tasks. To address this issue, we first quantitatively investigate the impact of different feature and network layers on machine vision performance, providing a principled understanding of their importance. Based on this analysis, an Importance Guided feature and network Dual Adaptation (IGDA) framework is developed for ICMH. It adopts implicit neural representation as the basic architecture and designs an efficient adaptation structure with both feature and network weight adaptation. An Importance-Guided Network Adaptation (IGNA) module is developed with a low-rank adaptation strategy to selectively adjust key network layers according to their importance in different tasks. An Importance-Guided Feature Adaptation (IGFA) module is developed with a low-rank and Kronecker product-based transform to explore the non-local property of the features in order to reduce the number of adaptation parameters. Extensive experiments on representative machine vision tasks, including image classification and object detection, demonstrate that our IGDA-ICMH framework achieves significantly better performance than the existing implicit neural representation-based methods for ICMH.
comment: Published in IEEE TCSVT
☆ Movens-Zero: Scaling Humanoid Motion Foundation Models with Large-Scale Human Videos
Wendong Bu, Tinghong Ye, Qizhou Wang, Jiacheng Li, Zhiqi Ge, Yuze Lin, Zhongqi Yue, Yi Su, Jingyu Wang, Kai Shen, Jingdong Liu, Chengyuan Deng, Xiaoxuan Chen, Xin Tan, Jiaming Ji, Wenqiao Zhang, Chenghong Jiang, Tuoyu Li, Siliang Tang, Jun Xiao, Tat-Seng Chua, Yueting Zhuang, Xingxing Wang, Juncheng Li
Scaling humanoid motion foundation models is constrained by the scarcity of diverse, robot-executable data and by the difficulty of jointly learning from heterogeneous multimodal inputs while supporting real-time closed-loop control. We present Movens-5M, constructed from raw Internet videos through clip generation, quality filtering, semantic annotation, motion recovery, and physical refinement with humanoid retargeting and execution verification. It contains 15 million human-motion sequences and 5 million physically executable humanoid-motion sequences spanning over 10,000 hours, aligned with text, RGB video, speech, and music. Building on this data, we introduce Movens-Zero, a scalable understanding-generation-execution architecture that supports joint learning across modalities and real-time closed-loop execution. Task-mixed flow-matching training jointly optimizes a single action expert across seven generation tasks, enabling direct motion generation from text, speech, music, and video. Multimodal Real-Time Chunking connects overlapping chunks under continuously updated instructions and robot states for continuous closed-loop execution. We further introduce Movens-Intent, a four-track benchmark with 8,192 expert-verified condition-motion pairs across text, speech, music, and video. Experiments show consistent gains across all four modalities when scaling execution-verified data from 1% to 100% and action-expert capacity from 64M to 911M parameters, while demonstrating that data quality and cross-modal supervision improve generation and execution.
comment: Technical report. 86 pages, 31 figures, 15 tables. Project page: https://movens-zero.github.io
☆ Open-Weight MLLMs Cannot Compare What They Cannot Name
Multimodal Large Language Models (MLLMs) are increasingly capable of understanding images and performing complex visual reasoning, suggesting the possibility of using them to automate biological image analysis. This need is becoming increasingly urgent as advances in technology continue to generate ever larger and more diverse microscopy datasets, already exceeding the scale at which manual visual analysis is feasible. With the final aim to use MLLMs for visual comparison of complex phenotypes, we investigate a very simple baseline: comparison of the size of body parts in synthetic images of ants. We find that state-of-the-art open-weight MLLMs fail for some body parts, but not for others, even though the comparison itself is visually trivial for all of them. We evaluate different in-context learning strategies and analyze performance on the related object recognition task. Our results suggest a recognition-driven grounding failure: structures that the models cannot recognize are instead associated with a recognizable semantic concept, impairing the grounding and comparison of both. Further analysis of the vision encoder is consistent with the hypothesis that fine-grained visual structure may be less well preserved without semantic support. Together, our results reveal a surprising limitation of current MLLMs: semantic recognition can gate simple visual comparisons that do not intrinsically require semantic understanding. Our code and data are available at https://github.com/kreshuklab/Open-Weight-MLLMs-Cannot-Compare-What-They-Cannot-Name
☆ ReuScope: Auditing Data Reuse and Evaluation Sensitivity in Capsule Vision 2024 NeurIPS 2026
We develop ReuScope, a protocol for auditing data reuse and evaluation sensitivity, using Capsule Vision 2024 as a case study. It combines measurements of data relationships with two comparisons: changing the evaluation population while holding predictions fixed, and changing training data while holding evaluation fixed. Byte-identical files cross the public training-validation boundary, and all 41 Kvasir-Capsule recordings that contribute frames occur in both partitions. Shared source recordings establish cross-split overlap; missing cross-recording patient identifiers prevent counting unique shared patients. Across ten paired seeds, removing all training rows attributed to Kvasir-Capsule, plus five rows from other sources flagged as near-matches to it, lowers public-validation balanced accuracy from 82.5% to 61.2%. Evaluating the same fitted models on the validation rows of the other sources reverses the comparison: removal improves balanced accuracy by 1.38 percentage points. The public-validation drop measures sensitivity to source removal, not an amount of leakage-induced inflation. Matched controls show that sample size and class counts alone do not explain the observed score differences. An exploratory experiment removes the training neighbors of validation frames that have close training matches, or equally many distant training rows, and yields a difference-in-differences estimate, relative to matched unflagged frames, of -0.355 percentage points in true-class probability; the paired-t interval for accuracy spans zero. The resulting procedure distinguishes observed reuse from conditional performance sensitivity and specifies the population, class support and uncertainty unit for each conclusion.
comment: Accepted at NeurIPS 2026, Track on Evaluations and Datasets. 39 pages, 3 figures. Code and data: https://github.com/eunseobchoi/ReuScope
☆ ObjectGuidedMap: An Object-Aware Model for Robust Bird's Eye View Map Prediction in High-Occlusion Scenarios
Accurate BEV map prediction is a critical component of autonomous driving systems, enabling downstream planning and control. Recent approaches such as Mask2Map achieve strong performance by predicting vectorized, instance-level map elements directly from multi-view images. However, existing BEV map prediction models operate largely independently of object-level scene understanding, despite the fact that traffic participants frequently occlude or implicitly encode information about road geometry. In this work, we propose a multitask architecture that jointly predicts BEV maps and 3D objects, explicitly enabling information exchange between the two tasks. Building upon Mask2Map, we introduce an object detection branch and incorporate cross-task feature fusion at both the BEV feature level and the instance-query level via deformable attention and cross-attention mechanisms. This design allows map instances to leverage object context while remaining robust to occlusions caused by traffic participants. Experimental results on the nuScenes and Argoverse 2 datasets demonstrate that our approach consistently improves BEV map prediction accuracy, with particularly significant gains of 21% mAP in scenes containing heavy object occlusion.
☆ PGGT-Loc: Primitive-Grounded Geometry Transformer for Feed-Forward Camera Localization NeurIPS 2026
Camera localization methods are mostly multi-stage pipelines relying on dedicated and complex maps that are costly to construct, store, and maintain over time. Alternatively, well-structured maps that organize scenes with compact yet expressive primitives are often readily available, providing rich structural and semantic cues that humans can interpret. However, their potential for direct camera localization remains largely unexplored due to the significant modality gap between map primitives and visual observations. This motivates a new concept for localization, which estimates camera poses by grounding query observations to map primitives. In this paper, we instantiate the concept in the context of planar primitives with PGGT, the Primitive-Grounded Geometry Transformer network. Given a single query image and a set of planar map primitives, the network is trained to predict multiple localization targets for feed-forward 6-DoF camera localization that generalizes to unseen environments. With this straightforward and scalable architecture, our method pushes the state-of-the-art localization performance on both dense surface maps and minimal layout models. The code and models are available at https://github.com/3dv-casia/PGGT-Loc
comment: Accepted at NeurIPS 2026. 24 pages, 14 figures, including appendices. Code and models: https://github.com/3dv-casia/PGGT-Loc
☆ A Patch is More Than a Token
Vision Transformers represent each image patch through a canonical token produced under one observation condition. That token is a powerful interface, but it is still one realized state of the patch. We introduce PMTT, which establishes two foundations: a patch representation object can extend from one canonical state to a canonical-centered Representation State Family, and identity-preserving variation opens Patch-Level Local Feature Space. We develop four parts of this representation program. Patch Information Acquisition and Dense Orbital Views expose lawful same-patch states. Visible-to-Hidden prediction tests whether observed states reveal structure beyond themselves. Intrinsic analysis identifies low-dimensional, energy-concentrated, patch-specific, predictive geometry. Representation Organization separates acquiring more information from organizing that information, with an analytic Manifold as the central structured representation. We realize PMTT on frozen DINOv3 S/B/L/H+ backbones without neural post-training of the representation. Across two independent 4,096-anchor cohorts, Manifold explains 70.8-79.6% of Hidden variation, compared with 55.0-72.3% for directly Observed states, and wins on more than 95% of anchors at every scale. Effective rank falls from about 2.5 to 2.0 while dominant energy concentration rises with backbone scale. Across six visual capabilities and four scales, Observed exceeds the canonical Center in 21 of 24 conditions, Manifold exceeds Center in 23, and Manifold exceeds Observed in 16. The strongest current consequence appears in physical correspondence, where MegaDepth satisfies Center < Observed < Manifold at every scale. These results show that pretrained visual backbones contain richer patch information than the standard single-token interface directly exposes. Code and experimental assets are available at https://github.com/zixiongwang-ai/PMTT.
comment: 54 pages, 16 figures, 12 tables. Code and experimental assets: https://github.com/zixiongwang-ai/PMTT
☆ Different Models, Shared Failures: Auditing Frozen-Encoder Pools for Medical Segmentation NeurIPS 2026
Average segmentation accuracy and model count do not reveal whether models fail on the same cases. We present CoFailMed, an audit of correlated failures in pretrained-encoder pools for medical segmentation. Across five 2D tasks, we evaluate 11 public pretrained encoders (a controlled sample, not a census) using frozen features and separately trained $1\times 1$ readouts with four seeds per encoder. Shared-checkpoint pairs have the highest mean per-case Dice correlation. An expanded 17-encoder analysis shows that distinct checkpoints within the same encoder family remain more correlated than cross-family pairs. At the pooled 33rd-percentile Dice threshold, four-member pools drawn from distinct encoders have mean effective sizes of 1.56-2.92, estimated from failure correlations in pools with enough failures and passes per member. In the primary pool, richer readouts and fine-tuning narrow the Dice-correlation gap between pairs sharing a checkpoint (or the DINOv2-B/S release) and all other pairs. Yet dependence between different encoders can increase, and fine-tuning does not consistently increase effective pool size at 33rd-percentile thresholds recomputed for each training recipe. A smaller between-group gap can coexist with greater dependence. Pool evaluation should therefore report absolute dependence and threshold-specific effective size alongside accuracy and nominal pool size. The CoFailMed release provides aligned per-case traces, analysis scripts and study code to reproduce the central audit results.
comment: Accepted at NeurIPS 2026, Track on Evaluations and Datasets. 47 pages, 8 figures. Code and data: https://github.com/eunseobchoi/CoFailMed
☆ Hard Sample-Conditioned Decorrelated Attention for Robust Face Recognition WACV 2027
Multi-head attention in Vision Transformers (ViTs) allows independent attention patterns across heads, but without explicit regularization, heads may learn redundant patterns rather than complementary roles. Correlation-based penalties encourage head disentanglement, yet apply uniform regularization across all training samples. In face recognition (FR), hard samples whose embeddings lie close to competing identities are critical for optimization and generalization. Motivated by this, we propose ViT-HarDis, which conditions attention head disentanglement on sample difficulty: samples with high cosine similarity to negative class centers receive stronger head decorrelation, enforcing specialization where identity ambiguity is highest. Experiments show improvements over both the baseline and uniform disentanglement on most benchmarks. Our analysis shows that ViT-HarDis reduces the similarity of hard samples to competing class centers while decreasing inter-head attention similarity, and that applying disentanglement only to the final transformer layers outperforms regularizing the entire network. Trained on WebFace4M, ViT-HarDis achieves SOTA results among ViT-based methods on IJB-B, IJB-C, and TinyFace Rank-1, while achieving the best IJB-B and TinyFace results when trained on MS1MV3, improving FR without architectural modifications or additional inference cost. Code: https://github.com/TaharChettaoui/Hard-Sample-Conditioned-Attention-for-FR.
comment: Accepted at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2027)
☆ WorkIng: A Real-World and Synthetic Highway Work-Zone Benchmark Dataset for Autonomous Navigation WACV 2027
Highway work zones are critical edge cases for autonomous vehicles (AVs), combining atypical geometries and jurisdiction-specific regulations that existing benchmarks fail to capture: real-world datasets lack 360° multi-modal, full-scene 3D work zone annotation, and no single recorded site can span the layouts and regulatory variants needed for generalizable models. We present WorkIng, a dual-domain benchmark and training resource. WorkIng-Highway provides a 360° multi-modal work zone benchmark with synchronized 128-channel LiDAR, six RGB cameras, and RTK-GPS across 32 km, yielding 12,000+ annotated frames and 227,399 tracked 3D bounding boxes. WorkIng-CARLA complements this with procedural simulation spanning 24 regulation-aware scenarios across six canonical topologies and six international traffic standards, populated by 100+ custom 3D assets. Benchmarking representative 3D detection and end-to-end planning architectures shows that real-world data remains the primary performance driver, and that scenario-stratified synthetic augmentation improves architectures across 2D recognition, 3D detection, and trajectory planning. Resources: https://github.com/cvims/WorkIng
comment: Accepted to WACV 2027. 19 pages including supplementary material. (c) 2027 IEEE. Personal use of this material is permitted
☆ Measuring What Matters: A Causal Reliance Score for Histopathology Image Segmentation
Samuel Thomas, Daniel Tweneboah Anyimadu, Ahmed Karam Eldaly, Somaia Elsheikh, Mohammed M. Abdelsamea
Reliable histopathology segmentation requires models that rely on pathology-relevant structures rather than spurious background correlations. In this work, we argue that improving architectures alone is insufficient if models continue to rely on spurious correlations rather than pathology-relevant features. We introduce a causality-inspired evaluation framework based on controlled interventions that explicitly probes model sensitivity to foreground tissue structure and background/domain variation. Building on this, we propose the Causal Reliance Score (CRS), a metric that quantifies the relative sensitivity of model predictions to foreground perturbation versus background/domain variation under controlled interventions. To support this analysis, we design augmentation strategies that act as implicit counterfactual interventions during training, encouraging invariance to nuisance factors while preserving morphology. We demonstrate how these interventions reveal behavioural differences between models that are not captured by standard metrics such as Dice or IoU. While we instantiate our framework using a self-supervised transformer (CAUSE-ViT), the primary contribution of this work is not a new architecture, but a principled intervention-based methodology and evaluation metric for auditing model behaviour in medical image segmentation. Our results suggest that evaluating model behaviour through counterfactual interventions is essential for developing reliable and clinically robust medical image segmentation systems.
comment: 15 pages, 8 figures, accepted into Medical Image Understanding and Analysis (MIUA) 2026 conference
☆ SAR-TS-BERT: Physics-Guided Self-Supervised Pre-Training With Polarimetric Features for SAR Time-Series Crop Classification
Multitemporal synthetic aperture radar (SAR) imagery provides continuous, cloud-free observations of crop phenology for agricultural monitoring in cloudy and rainy regions, yet high-accuracy classification typically requires extensive labeled samples. Although self-supervised pre-training mitigates label dependency in remote sensing, existing frameworks primarily target optical imagery or generic spatial SAR scenes, underutilizing temporal backscatter dynamics and polarimetric scattering mechanisms. We propose SAR-TS-BERT, a physics-guided BERT-style self-supervised pre-training framework for SAR time-series crop classification. SAR-TS-BERT integrates backscatter intensities and polarimetric ratio features (VV, VH, and VV/VH) with dual-polarimetric Cloude-Pottier decomposition parameters ($H/α/A$) into a six-band input representation through two core designs: 1) a physics-guided span masking strategy (SpanPhysics) that adaptively selects contiguous masked spans based on temporal backscatter change rates to prioritize key phenological transitions; and 2) a dual-branch cross-modal fusion encoder that models backscatter and polarimetric decomposition features through separate Transformer branches followed by bidirectional cross-attention fusion. On independent test sets in Estonia (EuroCropsML) and the USA (California CDL), SAR-TS-BERT achieves overall accuracies (OAs) of 96.78% and 87.29%, respectively, and attains a mean monthly OA of 99.37% on Chongming Island, Shanghai. On the Estonia benchmark, pre-training improves OA and Macro F1 by 1.17 and 1.59 percentage points over training from scratch under full supervision, and by 2.58 and 5.19 percentage points using only 1% labeled data. Incorporating SAR scattering physics into temporal self-supervised pre-training effectively enhances crop classification accuracy, parcel-level mapping consistency, and few-shot transferability.
comment: 18 pages, 13 figures, 18 tables. Jieqiu Wang and Yuhan Zhang contributed equally to this work
☆ Nereus: A Large-Scale Underwater Dataset for Fine-Grained Attribute Understanding and Grounded Counting Perception NeurIPS 2026
Underwater imagery records rich ecological information and supports applications such as science communication, fishery monitoring, ecological conservation, and oceanographic research. Beyond general scene understanding, these applications require models to recognize fine-grained biological traits and reason over spatially grounded evidence. However, existing underwater datasets often provide coarse category labels, generic captions, or total-count supervision, which limits their use for organism-level analysis and ecological reasoning. We introduce Nereus, a large-scale dataset for Underwater Fine-grained Perception that jointly supports Fine-Grained Object Attribute Understanding and Grounded Counting Perception. Nereus contains 88K images and 2.68M question-answer pairs, including annotations for 136K marine organism objects across 2,690 species and 1,515 genera, as well as 65K point annotations and 67K grounded counting QA pairs. Experiments with representative multimodal models show that Nereus improves downstream adaptation over existing underwater-domain data and provides a foundation for future research on fine-grained marine perception.
comment: Accepted by NeurIPS 2026 Evaluations and Datasets Track
☆ Retrieval-Augmented Initialization with Dual-Guided Inversion for Flow-Based Inverse Problem Solvers WACV 2027
Flow models have recently emerged as powerful generative priors for solving inverse problems. Despite their success, most existing flow-based inverse problem solvers initialize the reconstruction process with random Gaussian noise. However, recent studies in image generation have shown that the choice of initial noise can significantly influence generation outcomes. The role of initialization as a distinct design choice in flow-based inverse problem solving remains comparatively underexplored. In this work, we systematically evaluate noise initialization strategies while holding the downstream flow-based inverse solver fixed. We propose Retrieval-Augmented Initialization with Dual-Guided Inversion (RA-DGI), a method that constructs improved initial noise by leveraging retrieved proxy clean images and dual-guided inversion. Extensive experiments across multiple inverse problems demonstrate that the quality of the initial noise substantially affects reconstruction fidelity and perceptual performance. RA-DGI outperforms standard random initialization and achieves superior results compared to inference-time scaling strategies, while requiring less computational overhead. These findings highlight initialization as a critical yet previously overlooked factor in flow-based inverse problem solvers.
comment: WACV 2027
☆ OT-CAL: Optimal-Transport-Guided Adaptive Sample Selection for Efficient Visual Learning IEEE
Deep neural networks for image recognition are typically trained using mini-batches, with a forward pass to compute predictions and losses followed by a more computationally expensive backward pass to update model parameters. However, not all training samples contribute equally to learning, suggesting that computation can be reduced by performing backward passes only on informative samples. The challenge is that sample informativeness changes throughout training: difficult samples may be valuable early on, improving samples become important later, and class diversity becomes increasingly relevant toward the end. Existing methods often rely on fixed selection rules or computationally expensive per-sample gradient calculations. We propose OT-CAL, an adaptive data selection method that automatically adjusts its sampling preferences during training. OT-CAL represents each sample using five informativeness features, including difficulty, learnability, and uncertainty. A curriculum vector describes the currently preferred sample characteristics, and optimal-transport-based matching assigns selection weights according to how closely samples match this target. The curriculum vector is then updated using the selected samples, forming a feedback loop that adapts without a manually defined schedule. Experiments on three standard visual learning benchmarks show that, when selecting 50% of each mini-batch, OT-CAL improves performance by 1.3--2.1 points over AdaSelection while achieving 1.18--1.24 times faster training than full-data training.
comment: 9 pages, 3 figures. Submitted to the IEEE BigData 2026 Workshop on Efficient AI
☆ A Self-Supervised Pose Estimation Approach for Construction Machines
Pose estimation is a computer vision task used to estimate a skeleton of dynamic systems to predict future movements. Most of the research in this direction is based on a supervised learning approach which requires a massive amount of labeled datasets. In this paper, a self-supervised three-stage model based on a contrastive learning approach is introduced for estimating a skeleton of dynamic construction machines; such as excavators without using any labeled images for the first stage. The whole model structure is divided into three stages: the pre-train stage using the SimCLR contrastive approach, and two fine-tuning stages for the transfer learning and the downstream task. The model can leverage the features and learn from a huge unlabeled dataset called ACID to two small datasets generated from NVIDIA Isaac and MATLAB Simscape simulators as well as transfer the knowledge to a smaller dataset with a ratio of 3.5% from the original ACID dataset. The results show that the proposed approach can improve the accuracy of pose estimation for heavy construction machines in real images by 11% and 13% in comparison to the normal self-supervised approach with two backbones ResNet-50 and HRNet-W32, respectively.
comment: 12 Pages, 12 figures, submitted for the International Symposium on Visual Computing conference
☆ OpenVIO: A Versatile Open-Source Visual-Inertial Odometry System
Visual-inertial odometry (VIO) is a fundamental topic in robotics research. Beyond research, VIO has been adopted in a wide range of products. In this manuscript, we present OpenVIO, an open-source VIO system for research and practical applications. It is released under the MIT license. OpenVIO supports pinhole and fisheye cameras and extends this support to stitched 360° images. We describe each part of the system together with its design considerations. We also report benchmark results with other open-source VIO systems on public datasets. OpenVIO is available as open source at https://github.com/93won/openvio.
comment: 7 pages, 6 figures
☆ Enhancing Medical Lesion Segmentation via Several Simple Modules
This work introduces three lightweight enhancement modules for medical lesion segmentation to improve clinical applicability, particularly under data-scarce and computationally constrained environments. Current mainstream methods struggle with the lack of high-quality annotations for rare pathologies, which often leads to severe morphological distortions during inference, despite achieving seemingly ideal DSC scores. Furthermore, the isolated development of individual segmentation tasks restricts cross-domain methodological innovation, underscoring the need for leveraging shared knowledge across different lesions within the same anatomical region. However, constrained access to historical data and annotations hinders efficient knowledge extraction and triggers catastrophic forgetting when adapting to new classes. To address these challenges, we first propose DT-Morph Combined Loss with continuous spatial pooling to optimize differentiable topological structures and boundary continuity. Second, we introduce 3D Group Prompt, which extracts domain-universal knowledge via curated volumetric image-mask alignment prompts together with text prompts built on SAT baseline. Finally, we replace conventional output layers with Many-to-One Conditional Segmentation Head equipped with synonym-resolved routing, which brings negligible computational cost and enables fast incremental learning for new categories without sacrificing performance on old tasks. Ablation studies on abdominal and extra-abdominal datasets covering both organ and lesion segmentation verify the efficacy of our modules.
comment: 16 pages, 6 figures, 5 tables
☆ When Depth Lies: Benchmarking Vision-Language Models on Mirror-Induced RGB-D Ambiguity NeurIPS 2026
Vision-language models (VLMs) have made significant progress in spatial reasoning, particularly by integrating RGB images with depth data, which is often assumed to faithfully reflect physical distances. However, mirrors violate this assumption: they distort depth readings while reproducing object appearances, making reflections difficult to distinguish from real objects. As existing benchmarks rarely consider such reflective scenarios, VLM performance in mirror scenes remains largely unexplored. In this work, we introduce a mirror-centric RGB-D dataset from reflective scenes to evaluate the spatial reasoning capabilities of state-of-the-art VLMs in these scenarios. Specifically, we collect 1,011 RGB-D images from 525 real-world scenes, spanning diverse indoor and outdoor environments, six reflective surface types, and both daytime and nighttime conditions. All images are annotated with reflective surface region masks and mask annotations for both directly observed and mirror-reflected objects. Then, we curate a benchmark named MIRA-Bench (Mirror-Induced Reasoning Ambiguity Benchmark), comprising 14,140 QA pairs across eight sub-tasks organized into three cognitive levels: Reflection-aware Perception, Spatial Reasoning, and Scene-level Decision-making. We evaluate state-of-the-art VLMs on MIRA-Bench and find consistent failures across tasks: models misclassify whether objects are directly observed or reflected, fail to match reflected instances to their real-world counterparts, and produce erroneous spatial inferences. Remarkably, providing depth data actually degrades reasoning performance, implying that VLMs cannot identify incorrect depth in mirror scenes. The code and data are available at https://github.com/EvaYin-cloud/MIRA-Bench
comment: NeurIPS 2026 E&D Track accepted
☆ TADP: Dual-Branch Temporally Adaptive Differential Privacy for Expression-Aware Video Anonymization WACV2027
We present TADP, a temporally adaptive differential privacy (DP) framework for inference-time privacy-preserving video generation under fixed-length clip-replacement adjacency. TADP uses a dual-branch design to extract identity and expression representations, clips and privatizes both branches with temporally adaptive Gaussian mechanisms whose noise scales depend only on preceding privatized releases within the same clip and are bounded below by history-uniform calibrated minima, and accounts for their joint release via adaptive Rényi Differential Privacy (RDP) composition. The clip-level RDP guarantee is converted to $(\varepsilon,δ)$-differential privacy, while soft quantization and subsequent temporal processing are applied only after privatization as DP post-processing. Experiments on facial expression datasets show reduced identity leakage under evaluated attacks, usable perceptual quality and temporal stability, and high facial expression recognition utility relative to the evaluated privacy-preserving baselines. TADP formally satisfies clip-level DP and empirically provides a favorable privacy--utility trade-off under the evaluated threat models and datasets.
comment: WACV2027 accepted
☆ GRIT: Gradient Masking for Visually Misgrounded Tokens in Text-Rich Reasoning WACV 2027
Reinforcement learning with verifiable rewards (RLVR) has recently been extended to vision-language models (VLMs), yet its effectiveness remains limited for text-rich reasoning tasks. We highlight a key factor contributing to this limitation: models frequently arrive at correct final answers despite misperceiving visual information during intermediate steps. We define this phenomenon as Correct-but-Visually-Misgrounded Reasoning (CVMR). Because standard RLVR rewards only final-answer correctness, it fails to differentiate between properly grounded and misgrounded reasoning, reinforcing both equally. Through token-level analysis, we reveal that CVMR tokens exhibit distinct attention patterns that differ systematically from well-grounded ones. Based on this observation, we propose GRIT, a training framework that incorporates visual grounding into RLVR to mitigate the impact of CVMR. Furthermore, to address the lack of text-rich reasoning benchmarks that allow for systematic evaluation across text length and reasoning depth, we introduce GRIT-Bench. Experiments demonstrate that GRIT significantly improves both reasoning accuracy and grounding fidelity over existing RLVR methods.
comment: Accepted to WACV 2027
☆ Attention Maps Know When to Think: Training-Free Difficulty-Aware Chain-of-Thought for Reasoning Segmentation WACV 2027
Chain-of-Thought (CoT) prompting has become a standard reasoning strategy in Multi-modal Large Language Models (MLLMs), showing strong performance in complex vision-language tasks such as reasoning segmentation. However, our analysis reveals that indiscriminate CoT usage can lead to degraded performance by inducing unnecessary reasoning, a phenomenon we term overthinking. In reasoning segmentation, overthinking often leads to misalignment between the query's intent and visual grounding. To address this, we propose Difficulty-Aware Chain-of-Thought (DA-CoT), a training-free adaptive framework that selectively triggers CoT based on the input's intrinsic difficulty. We find that the entropy of the model's attention maps reliably reflects task difficulty: low entropy indicates confident, localized grounding, while high entropy suggests uncertainty and potential benefit from CoT. DA-CoT uses this entropy signal to determine whether to apply CoT prompting. Additionally, we introduce a Bidirectional Rethinking mechanism that enables the model to automatically flip its reasoning decision when the initial output fails, enhancing robustness by recovering from failures induced by overthinking and underthinking. DA-CoT demonstrates its effectiveness by achieving state-of-the-art performance on the Reasoning Segmentation benchmarks while significantly improving inference efficiency.
comment: Accepted to WACV 2027
☆ DrawSplat: Atlanta World-Guided Implicit Gaussian SLAM for Indoor Reconstruction
RGB-D SLAM requires accurate online tracking, high-quality rendering, and geometrically consistent reconstruction. Existing Gaussian SLAM methods mainly optimize Gaussian primitives with photometric, depth, or local frame-to-model constraints, which are often insufficient in indoor scenes with weak textures, large planar structures, noisy depth, and long trajectories. The resulting maps may suffer from pose drift, surface thickness artifacts, noisy normals, and blurred structural boundaries. We propose DrawSplat, an Atlanta World-guided implicit Gaussian SLAM framework for indoor reconstruction. DrawSplat follows a drawing-like process: explicit point-line geometry first sketches the structural layout, and implicit neural Gaussians then fill in high-fidelity appearance and continuous geometric details. It estimates camera poses, sparse geometry, and gravity direction through point-line joint tracking, infers structural planes from tracked lines using the shared-gravity component of the Atlanta World prior, and converts them into differentiable regularization terms on Gaussian centers and normals. To improve compactness and efficiency, DrawSplat further combines an anchor-based implicit neural Gaussian representation with adaptive structural sampling, which allocates Gaussian capacity to structurally informative regions. Experiments on multiple datasets show that DrawSplat achieves robust tracking, high-fidelity rendering, and strong geometric reconstruction among Gaussian-based SLAM systems, while maintaining a practical balance among quality, efficiency, and model size.
☆ Scout-Refine-Correct: Teaching Flow Models to Refine Their Own Samples
The quality of image generation models trained with diffusion or flow matching is largely fixed by the training budget: at inference, the only available lever is the number of sampling steps, and additional steps improve quality only up to a point. We present a training recipe, Scout-Refine-Correct (SRC), that instead gives a flow-matching generator a genuine inference-time refinement capability. At each sampling step the model takes a scout rollout -- $K$ steps ahead along its own trajectory -- extrapolates a full-sample estimate, and feeds that estimate back to itself as a self-conditioning signal before taking the real step. This can be repeated for $R$ refinement rounds. Two correction losses, applied during training at states the model's own rollouts actually reach rather than only on the ground-truth path, teach the model to use this signal to reach new samples instead of collapsing to a suboptimal one. On ImageNet-256, SRC improves LightningDiT-B from FID~14.5 to FID~7.26 at NFE~550, matching a privileged best-of-$N$ baseline that uses an oracle FID verifier to choose among samples. SRC uses no external verifier, no reward signal, and, after ablation, almost no extra parameters over the base architecture. Combining SRC with best-of-$N$ improves the result further, to FID~3.81 at NFE~4400, showing that the two axes are complementary. We ablate the main design choices -- the architectural pathway for the scout signal, the scout horizon, each correction loss, and the training schedule to demonstrate key working components of training.
comment: preprint
☆ TopoHyperClinicalGraph: A Geometry and Topology-Aware Multimodal Framework for Cancer Survival Prediction
We developed TopoHyperClinicalGraph, a multimodal framework combining hyperbolic tissue geometry, persistent topology and clinical graph learning for cancer survival prediction. Pre-extracted UNI2-h features are reduced from 1,536 to 1,024 dimensions using training-fold PCA. A pathology backbone combines hyperbolic neighborhoods with Lorentz-centroid attention pooling. Once frozen, its survival logits are adjusted using persistence descriptors from up to 64 landmarks per slide and sampling controls. A five-node clinical graph then learns a further correction from age, sex, stage, T and N.
Five-fold patient-level evaluation covered eight TCGA and CPTAC cohorts with 2,364 patients. Mean validation-selected C-indices for TopoHyperClinicalGraph versus pathology-only spatial PatchGCN were 0.7563 versus 0.7386 in COADREAD, 0.6220 versus 0.5998 in HNSC, 0.6755 versus 0.6535 in BRCA and 0.6409 versus 0.6176 in STAD. CPTAC scores were 0.5984 in HNSC, 0.6060 in PDA, 0.8055 in CCRCC and 0.7192 in LUAD. CPTAC-HNSC used TCGA-initialized adaptation; other CPTAC backbones were trained within their cohorts. Geometry accounted for most pathology-only gains, with smaller persistence effects. Clinical graph fusion exceeded its matched node-wise control in five cohorts, tied in one and scored lower in two; clinical-only prediction exceeded fusion in three CPTAC cohorts.
The framework supports separate evaluation of geometric, topological and clinical contributions. All scores were validation-selected; independent testing is needed to establish generalizability.
☆ TACO: Trajectory-Aware Curvature Observation for Memorization Localization and Mitigation in Diffusion Models
Text-to-image diffusion models can reproduce memorized training content within localized image regions, making precise localization essential for targeted mitigation. However, curvature observations at a single denoising state can contain transient responses, while spatially uniform mitigation may disrupt prompt-relevant content. We present Trajectory-Aware Curvature Observation (TACO), a unified framework for local memorization detection and mitigation in diffusion models. Our empirical analysis reveals that memorization-related curvature becomes pronounced during late-stage denoising and persists at consistent spatial locations across neighboring states. Motivated by these observations, TACO aggregates prompt-dependent curvature responses along the late denoising trajectory and applies latent-guided spatial refinement to improve spatial coherence. The resulting memorization map serves both as a localization signal and as a spatial control signal for mitigation. Specifically, TACO uses this map to steer existing inference-time interventions toward memorization-related regions through spatial reweighting and localized guidance correction. Extensive experiments demonstrate that TACO consistently outperforms competing localization methods, and improves the Pareto trade-off between memorization suppression, semantic alignment, and perceptual quality. These results establish trajectory-aware spatial analysis as an effective link between memorization localization and targeted mitigation.
comment: 19 pages, 10 figures
☆ Parameter-Efficient Fine-Tuning of Transformer Encoders for Audio-Image-Symbolic Music Retrieval
Score-based multimodal music retrieval requires representations that are both discriminative within each modality and compatible across audio recordings, sheet music images, and symbolic scores. This paper investigates the effectiveness of parameter-efficient fine-tuning (PEFT) for adapting pretrained music encoders and examines whether modality-specific improvements translate into better multimodal representations. We compare full fine-tuning, LoRA, and QLoRA across nine pretrained audio, image, and symbolic score encoders on TriScore and the Musical Theme Dataset. Encoders are first evaluated and selected through supervised unimodal classification, using frozen-encoder linear probing as a reference. We then introduce a controlled 2x2 design crossing encoder adaptation with task-specific shared projection, comparing frozen and adapted representations before and after projection. LoRA or QLoRA is selected in 14 of 18 modality-target combinations, frequently matching or outperforming full fine-tuning while optimizing substantially fewer parameters. However, these unimodal gains do not transfer uniformly to multimodal classification. Adaptation and projection are complementary for composer classification on TriScore, largely redundant for TriScore instrument and the Musical Theme Dataset instrument and ensemble tasks, and partially interfering for composer classification on the Musical Theme Dataset. The final adapted-and-projected title representation on TriScore supports piece-level retrieval, reaching average Top-5 and Top-10 accuracies of 55.32 and 64.68, respectively. Cross-modal exclusion and neighborhood analyses further reveal strong modality-specific local structure in the projected spaces. Overall, PEFT provides an effective and computationally efficient alternative to full fine-tuning, but improved unimodal discrimination alone does not guarantee better multimodal organization.
☆ VoxLution: Evolving Voxels for High-Fidelity 3D Scene Generation
High-resolution 3D indoor scene generation is crucial for immersive applications such as virtual reality, spatial computing, and robotics simulation. We introduce VoxLution, a coarse-to-fine transformer-based approach that generates high-fidelity indoor scenes with rich details, overcoming the overly smooth geometry typical of existing generative models. First, we generate a low-resolution spatial layout; then, based on this structural information, we progressively refine the scene geometry locally in chunks. Key to our approach is formulating 3D super-resolution as a sequence modeling problem over multi-resolution sparse voxel grids. To achieve this, we introduce a training-free, resolution-invariant tokenization scheme that compactly encodes the geometric information of eight octants into a single decimal number. We formulate the transformer sequence to include multiple resolutions, allowing the coarse layout to guide the generation of fine local details. To ensure coherence across local chunks, we incorporate adjacent chunk boundary information directly into the transformer sequencing. VoxLution achieves scene generation at a finer voxel resolution than existing baselines, demonstrating state-of-the-art performance on standard benchmarks with 59.6% lower FPD and 51.1% lower FID on 3D-FRONT, and 70.5% lower FPD and 73.8% lower FID on Scannet++.
comment: Project page: https://daoyig.github.io/VoxLution/; Video: https://youtu.be/0DAaquV1xK8
☆ VariaFlow: Uncertainty-Aware Flow Matching for Unsupervised Anomaly Detection WACV 2027
Reconstruction-based anomaly detection no longer fails at reconstruction. Generative models produce high-quality pseudo-healthy reconstructions, and the remaining difficulty is the step of scoring residuals. The residual between a scan and its reconstruction has two indistinguishable sources, genuine pathology and the anatomical variation that separates one healthy brain from another. Scoring against a single reconstruction treats both as anomalous, inflating false positives where healthy anatomy varies most while small or subtle lesions are lost in the surrounding reconstruction error. We identify the missing quantity as the predictive variance, how much a healthy reconstruction can vary at each location. We introduce VariaFlow, which estimates this variance from an ensemble of mask-guided restorations under an unconditional flow matching prior, each started from a different noise initialization, and which normalizes the residual by the resulting standard deviation. Few-step flow matching keeps the ensemble computationally affordable, and restoring at two spatial scales captures lesions of different size. Across three brain MRI datasets spanning stroke and tumor, VariaFlow is trained once on healthy anatomy and generalizes across all three, attaining the highest AUROC (0.94, 0.80, 0.78) on each and the highest AUPRC on both cross-domain datasets, exceeding the strongest baseline by 37% and 50%. Under domain shift, the same baseline drops by up to 52% in AUPRC, whereas VariaFlow stays more stable and achieves the best small-lesion Dice on all three datasets.
comment: Accepted for WACV 2027
☆ BDNet: Fast-slow thinking is all you need
We present BDNet, a hierarchical image-classification backbone built around fast image features and slow statistical references. Its baseline deformation operator (BDO) transforms the deviation of a feature from a stage-specific reference through a low-rank projection and a learned channel scale. Rather than computing pairwise token similarity, the branch operates directly on baseline-relative residuals with complexity linear in the token count. Each of four reference buffers accumulates detached stage statistics during training and remains frozen at inference. A four-stage pyramid with depths $(2,2,6,2)$ and widths $(96,192,384,768)$ yields a 20.70M-parameter classifier at $224\times224$ resolution. We organize an ImageNet-1K study around clean classification, eight controlled ablations, and Gaussian-noise, contrast, and brightness perturbations. The central hypothesis is that a slow reference can improve the robustness of a compact representation while retaining a simple tensor implementation. We distinguish this training-time statistical mechanism from input-dependent attention and make its linear inference behavior explicit.
comment: 7 pages, 2 figures, this manuscript is an early draft
☆ Beyond Trial-and-Error: A Geospatial-Guided Active Reasoning Framework for Robust Aerial Vision-and-Language Navigation
Jiawei Ge, Wenkang Gong, Jiuxin Cao, Bo Liu, Lei Cui, Haoran Wang, Xuelin Zhu, Biwei Cao, Chang Liu, Chen Feng, Ioannis Patras
Aerial Vision-and-Language Navigation (VLN) requires UAVs to locate language-specified goals in large-scale urban environments, where objects often appear small and visually ambiguous from high-altitude viewpoints. Existing methods largely follow a reactive paradigm common in indoor VLN, repeatedly updating navigation states from local observations while underutilizing the geospatial structure that remains stable during navigation. As a result, visually similar distractors can cause a reactive goal search to repeatedly reinforce locally plausible goal hypotheses based on passively accumulated evidence, leading to unreliable localization. To address this, we propose GeoReasoner, a robust framework that shifts the paradigm of Aerial VLN from reactive trial-and-error to geospatial-guided active reasoning. Specifically, GeoReasoner first performs pre-search geospatial reasoning to derive region-level priors and constrain the goal hypothesis space. Building on these, it further conducts hypothesis-driven exploration to actively acquire discriminative evidence and refine competing goal hypotheses. Finally, consistency-aware goal verification adversarially assesses complementary evidence sources before stopping, hence reducing premature or erroneous goal confirmations. Experiments on the real-world CityNav benchmark demonstrate that GeoReasoner sets a new SOTA among zero-shot Aerial VLN methods while competing favorably with supervised approaches.
☆ FlexReader: Towards Adaptive Decoding Paths for Efficient Arbitrary-Length Scene Text Recognition
Existing Scene Text Recognition (STR) models are inherently coupled with a specific decoding strategy by their training paradigms, limiting their flexibility in deployment across diverse recognition scenarios. In this paper, we propose FlexReader, an STR model that supports multiple decoding strides, enabling a single model to achieve an optimal accuracy-efficiency trade-off across different datasets. Specifically, we first introduce a Sub-string Perception module to perceive the target sub-string from visual features, followed by multiple parallel decoding blocks that independently decode each character in the sub-string. During inference, FlexReader supports decoding with arbitrary strides within the perceived sub-string range. Furthermore, we propose Adaptive Prefix Decoding, which adaptively selects the decoding stride at each step based on the model's confidence in the decoded characters, thereby identifying the optimal decoding path for each input. Experimental results demonstrate that FlexReader exhibits robust recognition capability across a diverse range of benchmarks, including those featuring long text, curved text, and occlusions. It consistently outperforms existing decoding strategies in recognition accuracy while achieving up to 6-flod decoding speedup over autoregressive decoding.
comment: 11 pages, 5 figures
☆ SACQ: Structure-Aware Codebook Quantization for Ultra-Low-Bit Vision-Language-Action Models
Vision-Language-Action (VLA) models enable capable robot behavior, but their large memory footprint limits deployment on resource-constrained robotic platforms. Weight quantization is a natural remedy, yet post-training quantization (PTQ) recipes inherited from large language models degrade when applied to VLAs at ultra-low precision. Existing low-bit PTQ methods often fail to explicitly account for the structure of downstream operations and allocate precision without considering its effect on the policy actions. We introduce SACQ, an ultra- low-bit weight-only vector-codebook quantization framework for VLAs. SACQ combines (1) structure-aware codebook fitting with role-specific quadratic metrics that reflect input statistics and downstream sensitivity; (2) action-aware mixed-bit allocation based on the increase in final-action error as individual layers are compressed; and (3) two-stage post-quantization refinement that reassigns codeword indices with second-order error compensation and calibrates codebooks and row-group scales against the decoded action. Using GR00T N1.7, SACQ achieves 93.85% average success across four LIBERO suites at 1.5 bits per language/action weight while reducing model memory by 4.83x. With INT8 activation quantization and low-precision attention, it retains 93.55% success while running 1.51x faster than BF16. On SimplER, SACQ further achieves 63.76% and 52.27% success under Visual Matching and Variant Aggregation at 2 bits per language/action weight, compared to 58.90% and 53.45% for BF16, respectively.
☆ DUGO: Dual-Scale Gaussian Memory for Online Temporal Action Localization in Egocentric Procedural Videos IEEE
Mohammed Aman Bhuiyan, Md. Sazzad Hossain Adib, Samiul Basir Bhuiyan, Mir Faiyaz Hossain, Syed Mohammed Shamsul Islam, Md Zakir Hossain, Shafin Rahman
Online Temporal Action Localization (OnTAL) aims to detect and localize action instances from untrimmed streaming videos in real time. Unlike offline approaches, OnTAL models have no access to future frames and cannot go back and refine earlier predictions, which makes the task considerably harder given the strict real-time constraints and narrow temporal window available. Most existing OnTAL methods lean on either brief anchor-based context or a single-scale memory of past frames, which limits how well they can capture actions unfolding across multiple time scales or requiring long-range reasoning in Procedural Videos. To address these gaps, we present DUGO (Dual-Scale Encoding and Gaussian Memory Online), a new OnTAL framework that jointly models local motion dynamics and long-range temporal dependencies. At its core, DUGO uses a Dual-Scale Temporal Encoder, pairing depth-wise local representations with global self-attention so it can pick up both fine-grained temporal patterns and broader contextual cues. We also introduce a Gaussian Memory Bank, which represents action memory as stochastic, input-conditioned Gaussian prototypes, allowing for richer temporal memory through gated cross-attention applied at both the encoder and anchor-query stages. Extensive experiments on procedural egocentric datasets (EGTEA, EPIC-Kitchens-100) demonstrate that DUGO achieves state-of-the-art performance while also providing substantial improvements on non-egocentric benchmarks such as Muses. The project page is available at: https://aman554-eq.github.io/DUGO
comment: This paper has been accepted at the 2026 IEEE International Conference on Big Data (IEEE BigData 2026)
☆ Parse Before You Execute: Efficient Spatial Reasoning with Executable Primitives
Spatial reasoning in Vision-Language Models (VLMs) has increasingly benefited from tool-integrated pipelines that ground computation in explicit geometry. However, existing systems often compile queries into relatively open-ended artifacts, such as code, task constraints, tool plans, or gathered evidence, leaving what must be resolved before execution under-specified. This can lead to unnecessary tool acquisition and iterative refinement, while still producing plausible reasoning traces from incomplete spatial commitments. We identify this failure mode as under-committed query compilation. To address it, we propose PEPS, a training-free agentic paradigm Parsing Executable Primitives for Spatial reasoning under a typed FESM schema over Frame, Entity, State, and Metric. PEPS follows a parse-before-execute principle: the Parser commits each query to a compact set of executable requirements; the Executor selectively grounds the required primitives through fixed geometric APIs and deterministic computation; and the Verifier checks execution sufficiency, enabling early termination or targeted repair. Extensive experiments across challenging spatial reasoning benchmarks show that PEPS consistently improves reasoning accuracy while substantially reducing refinement rounds, API cost, and end-to-end latency over strong tool-integrated baselines. These results demonstrate that explicit requirement commitment can make tool-integrated spatial reasoning not only more reliable, but also more execution-efficient.
☆ Queryable Gaussian View Index: Text Retrieval and Selective Camera Localization
We present Queryable Gaussian View Index (QGVI) for querying existing Gaussian scene assets and their associated calibrated reference data. Text-view retrieval and selective RGB camera localization return poses in the asset's coordinate frame. QGVI-T turns Gaussian-rendered views into a language-searchable index of scene locations, binding structured captions to known camera poses and place identifiers. Semantic and lexical retrieval select views from distinct places without online rendering, VLM calls, or a scene-specific pose network. The image route, QGVI-I, filters LoGS-based geometric estimates using distinct inlier query keypoints. It omits photometric refinement and preserves accepted pose coordinates. On nine development scenes, QGVI-T achieves 58.3% Top-1 and 78.3% Top-5 joint relevance-and-validity success, compared with 26.1% and 34.6% for our CityLoC reimplementation with refinement. QGVI-I achieves 97.3% scene-mean pose success at 1 m/2 degrees, with pooled false-acceptance and false-rejection rates of 0.707% and 1.142%. A paired cached evaluation retains all 1,775 initializer poses correct at this threshold. Recorded median text query latency is 0.020 s. Matched controls show competitive BM25 retrieval and slightly lower false acceptance for thresholded full LoGS; grid augmentation suggests modest gains on novel-view-derived descriptions. We report model-judged semantic success separately from source-camera recovery, with source-verified or estimated scales for geometric evaluation.
comment: Includes supplementary material; 3 figures, 47 references
☆ Task-Performance Routing for Multi-Teacher Distillation in CT Medical Image Segmentation MICCAI 2026
Pre-trained models offer complementary strengths in CT organ segmentation, but their relative accuracy varies across organs and spatial regions. Multi-teacher knowledge distillation can combine these strengths in a single student, yet global or instance-level teacher weights overlook variation within a 3D volume. We propose Task-Performance Routing (TPR), which uses region-wise segmentation errors to determine teacher weights during training without learned routing parameters. In hard regions, TPR favors teachers with lower prediction errors; in easy regions, it uses uniform weights to combine their knowledge. A hierarchical spatial partition provides fine-grained routing in shallow encoder stages and coarse-grained routing in deep stages. Experiments on BTCV, MSD-Liver, and MSD-Pancreas with two teacher pairs and two backbone architectures show higher Dice scores than the individual teachers, adaptive ensembles, and evaluated multi-teacher distillation baselines. On MSD-Pancreas, TPR achieves Dice scores of 65.40% with SwinUNETR and 68.25% with U-Net, compared with 64.38% and 67.45% for the corresponding adaptive ensembles. Inference uses the student alone, with no routing or teacher overhead. Code is available at https://github.com/EdvinCecilia/TPR_MultiTeacher.
comment: Accepted at MICCAI 2026. 11 pages, 3 figures, 4 tables. Code: https://github.com/EdvinCecilia/TPR_MultiTeacher
☆ Defining Page-Centric Visual Document Retrieval and Evaluating Existing Benchmarks
In modern information retrieval (IR), documents are increasingly encountered as structured sequences of visually rich, paginated units, rather than as flat text. Example documents encountered in our daily life include Word files, PDFs, and slide decks. Free text-centric retrieval systems either ignore this structure entirely or destroy it through arbitrary passage chunking. In this paper, we formulate page-centric Visual Document Retrieval (VDR) with pages as the fundamental indexing units and document context available for assessing page relevance. The formulation distinguishes two retrieval granularities, page and document, and three retrieval paradigms: independent-item, evidence-set, and iterative retrieval. We then assess 42 benchmarks using eight criteria covering corpus characteristics, query construction, relevance annotation, and ranking support. Our analysis shows two main findings on current VDR research. First, evaluation remains concentrated on page-level independent-item retrieval, with fewer resources supporting document-level, evidence-set, and iterative settings. Second, queries originating from real user information needs independently of target corpus content and graded relevance judgments are uncommon, while the coverage and verification of relevance judgments vary substantially. Both findings reveal gaps between the capabilities applications require and those existing evaluation protocols can establish. We provide benchmark selection guidance and identify priorities for future benchmark design, emphasizing alignment between task requirements, relevance semantics, and evaluation protocols.
☆ CalibSwitch: Observation-Driven Camera-LiDAR Extrinsic Recovery and Selective Maintenance for Multimodal Perception
Camera--LiDAR perception on mobile robots and intelligent vehicles depends on reliable sensor geometry throughout deployment. Installation variations create uncertainty in the initial extrinsics, while changing observation quality makes frame-wise estimates fluctuate during operation. This paper presents CalibSwitch, an observation-driven framework that separates absolute extrinsic recovery from selective state maintenance. A cross-modal absolute extrinsic generator (CAEG) combines projection-free bidirectional feature interaction with geometry-regularized learning to recover complete SE(3) candidates from image--point observations and coarse view context, without a supplied numerical initial transform. A selective recurrent calibration policy (SRCP) combines observation quality and temporal history to determine when a candidate should replace the working geometry consumed by a frozen multimodal detector. On nuScenes, CAEG achieves mean translation and rotation errors of 0.248 m and 5.884$^\circ$. Under sustained geometric transitions, SRCP reaches a mean composite error of 1.46 with 9.70\% candidate commitments, outperforming quality-based and temporal-consistency gating at comparable commitment rates. Relative to unconditional per-frame updating, it improves frozen-detector mAP and NDS by 1.43 and 0.92 points. Paired geometry--observation uncertainty tests, two real-vehicle platforms, and 13.5-FPS single-camera Jetson execution demonstrate the system value of selective geometric-state maintenance for continuous perception.
☆ Joint-Health-Aware Reinforcement Learning for Robust Robot Control Under Actuator Degradation
Robots deployed in unstructured environments are susceptible to actuator-authority degradation, which reduces the effective control authority of individual actuators and can cause reinforcement learning policies trained under nominal dynamics to fail. Existing robustness approaches rely on discrete fault labels, broad domain randomization, or global latent variables, often without explicitly modeling continuous per-joint residual actuation capability. We propose a joint-health-aware reinforcement learning framework for robust robot control under simulated actuator degradation. Our framework encodes actuator condition in a continuous joint-health vector and models degradation as multiplicative attenuation of executed actions. A dedicated HealthNet estimator infers per-joint health from recent observations and actions, allowing the policy to redistribute control effort across healthy and degraded joints. We evaluate the method on Walker, Go2, Cassie, and Panda-based ManiSkill manipulation tasks under diverse degradation masks and severity levels. The method outperforms nominal and degradation-trained no-health baselines on the main evaluated metrics. Relative to the nominal baseline, task-level locomotion returns improve by up to 250.1\% and manipulation success rates by up to 15.2 percentage points; in the high-degradation Cassie group, return increases from 3.94 to 40.50 (+928.0\%). Ablations support the contribution of per-joint health estimation, action-degradation modeling, and health-conditioned control.
☆ PRIN: A Perceptual-Structural Retinex-Inspired Network for Low-Light Image Enhancement
Low-light image enhancement (LLIE) focuses not only on visibility recovery but also on preserving structurally meaningful content and perceptual naturalness. Existing learning-based methods may achieve favorable pixel-wise fidelity while still producing over-smoothed textures, color deviations, or spatially incoherent illumination. Retinex-inspired methods provide interpretable decomposition into reflectance and illumination components, however, the asymmetric restoration roles of these components are rarely encoded through branch-specific frequency-domain refinement. To address this issue, we propose a Perceptual-Structural Retinex-Inspired Network (PRIN), which integrates Retinex priors with restoration-role-aware enhancement. PRIN employs a shared encoder and asymmetric decoders to estimate reflectance and illumination components. The reflectance branch performs attention-guided and edge-aware wavelet refinement to preserve textures and boundaries, whereas the illumination branch promotes spatially coherent brightness adjustment. After the recomposition of reflectance and illumination branches, a curve-based post-composition enhancement head calibrates residual tone and exposure deviations. Experiments on LOL-v1, LOL-v2-Syn, and LOL-v2-Real show that the proposed network ties for the best structural similarity index (SSIM) and obtains the second-lowest learned perceptual image patch similarity (LPIPS) on LOL-v1, achieves the best SSIM and LPIPS on LOL-v2-Syn, and obtains the highest peak signal-to-noise ratio (PSNR) and the second-lowest LPIPS on LOL-v2-Real.
☆ Bayesian PET Reconstruction with Learned Flow Matching Priors via Langevin Sampling SP
Image reconstruction for positron emission tomography (PET) is a challenging inverse problem due to its inherent ill-posedness and Poisson counting noise, particularly in low-dose acquisitions. In this work, we introduce a novel Bayesian PET reconstruction framework that leverages a flow-matching generative prior learned from high-quality PET images. By incorporating this prior, we formulate PET reconstruction as a Bayesian inference problem and solve it via latent-space posterior sampling using the unadjusted Langevin algorithm. We evaluate the proposed method through numerical experiments and benchmark it against classical reconstruction methods and state-of-the-art deep learning-based baselines. The proposed method achieves improved reconstruction performance while enabling posterior uncertainty quantification.
comment: Selected as the Best Student Paper Award for 2026 IVMSP
☆ With foundation-model features, aggregator complexity does not improve cross-hospital discrimination in gastric whole-slide image classification
Nheng Vanchhay, Ali Hussain, Khan Ziaullah, Moon-Il Joo, Md Ariful Islam Mozumder, Rashedul Islam Sumon, Won-Ae Lee, Min-Sun Cho, Hyeong-Chan Shin, Yujun Park, Baek Hwan Cho, Nam-Hoon Cho, Heejung An, Heung-Kook Choi, Haeyoun Kang, Hyunki Kim, Hee Cheol Kim
Whole-slide image classifiers are usually developed within one hospital and often go untested at unseen hospitals. We assembled 5,036 haematoxylin-and-eosin-stained gastric slides from five Korean hospitals, scanned at 20x and 40x, and compared multiple-instance learning aggregators under leave-one-hospital-out validation on frozen pathology foundation-model features. A hierarchical patch-region-slide graph aggregator (CHIME-MIL) with site alignment and two-scale fusion reached a mean area under the receiver operating characteristic curve (AUC) of 0.837, above six conventional attention and transformer aggregators trained without these steps. Under a matched recipe it did not exceed attention-based pooling, and global max pooling given the same alignment and fusion was better (AUC 0.855, P = 0.0002). The alignment, which subtracts each hospital's mean feature vector without labels, onboarded a new hospital from ten unlabelled slides and cost 0.002 AUC without held-out-hospital data, but its accuracy gains were small and inconsistent. The model had low top-label calibration error (0.053) but under-confident malignancy probabilities, and removing its most-attended patches lowered the malignancy probability far more than removing random patches. With these features, more complex aggregators did not improve discrimination; what a deployable system adds is label-free onboarding, calibration checks and auditable evidence.
☆ GenDSR: Transferring Representations from Video Generation Models for Dynamic Spatial Reasoning
Dynamic spatial reasoning is essential for connecting visual intelligence to thephysical world, yet remains challenging for vision-language models (VLMs), which must preserve scene state while tracking how spatial relations evolve over time. Meanwhile, video generation models (VGMs), trained to synthesize temporally coherent world dynamics, offer a rich source of spatiotemporal representations. We present GenDSR, a framework that transfers intermediate representations from VGM into VLM for dynamic spatial reasoning. Rather than treating a VGM feature as a monolithic prior, GenDSR constructs two complementary branches: a raw branch preserving holistic generative context and a delta branch emphasizing temporal variation. Task-conditioned tokenwise gates then fuse both signals and inject the result into the native visual representation. On DSR-Bench, GenDSR achieves 67.2%, surpassing the previous best result and establishing a new state of the art.
☆ STAR-FER: Adaptive Selective Restoration for Edge-Based Low-Resolution Facial Expression Recognition
Variable interaction distances continuously degrade facial resolution in edge-based affective interaction, making low-resolution facial expression recognition (FER) difficult under limited computation. Full-face image or feature restoration incurs substantial overhead, whereas lightweight recognition pipelines may miss local cues that remain discriminative after degradation. We propose STAR-FER, a scale-and-task-adaptive selective restoration framework that balances recognition performance and resource constraints. A lightweight image-scale conditioned Gate jointly predicts a pixel-wise spatial ranking and a continuous restoration budget, then applies exact-support projection to obtain an input-dependent free-form mask. The mask-conditioned restorer retains complete facial context while concentrating sparse restoration and residual insertion on the selected support. Across seven degradation scales from x2 to x8 on the complete RAF-DB test set, STAR-FER achieves 80.92% mean accuracy and 71.64% Macro-F1, improving the low-resolution ResNet-18 baseline by 2.25 and 2.71 percentage points, respectively. It surpasses SR-FER by 1.04 accuracy points while requiring 8.84 GFLOPs, a 54.29% reduction, with 28.01% mean mask coverage, and maintains effective recognition at unseen non-integer scales. These results show that spatial-ranking and continuous-budget driven selective restoration reduces spatial modification and computation while maintaining low-resolution FER performance, offering an efficient path to resource-constrained affective interaction.
☆ ART of PIV: Agentic Real-Time Particle Image Velocimetry for Active Flow Control
Particle image velocimetry (PIV) measures fluid motion from images of tracer particles, but its accuracy depends on choices throughout the estimation pipeline, including image preprocessing, flow estimation, and postprocessing. These choices are typically left to expert tuning, and a fixed configuration can become less accurate as conditions change. This matters especially when PIV supplies feedback for real-time control, where measurements must be both accurate and fast, and flow conditions vary. We introduce ART of PIV -- short for Agentic Real-Time and also a nod to the art of tuning a PIV pipeline -- which automates pipeline choice for each image pair. A learned agent selects a configuration according to a chosen measurement objective from a library assembled for a specified speed budget. Across cross-correlation, classical optical-flow, and learning-based estimators, ART of PIV reduces average end-point error (AEPE) by 12.2-22.4 % relative to the best single estimator at matched speed, with gains extending to 1000-4000 image pair/s. In a simulated local velocity regulation task, it reduces mean normalized closed-loop cost by 13.24-13.59 percentage points relative to a tuned fixed estimator, with the same classical controller in both cases. In a tabletop water channel, we illustrate two ways to tune ART of PIV for a fluidic pinball of three rotating cylinders: selecting between complementary optical-flow tunings for accuracy, and selecting regions of interest with coherent rotation or shear using a common flow estimator. For the first variant, we train a DreamerV3 reinforcement learning controller directly in the physical world and demonstrate tracking of a prescribed flow field in the wake of a fluidic pinball.
☆ SketchDirector: Complex Scene Sketching via Agentic Layout Reasoning and Detail Refinement
Existing sketch generation methods often rely on image guidance and are largely limited to individual objects or simplified scenes, leaving text-guided complex scene sketching underexplored. We present SketchDirector, an agentic framework that mimics the human sketching workflow for automatic multi-concept scene sketching. Given a scene-level text prompt, SketchDirector first employs a large language model~(LLM) to decompose the scene into multiple draft assets, where each is represented by a set of Bézier curves. Then, an agentic reasoning loop iteratively plans and executes their spatial arrangement, guided by self-evolving memory. Finally, a detail refinement module leverages diffusion priors to enhance sketch quality while preserving geometric structure. To support future research and applications in multi-concept scene sketch generation, we further introduce the \textit{MCSketch8K} dataset, comprising editable vector assets, compositional layouts, and object-level manipulation annotations. Extensive experiments show that SketchDirector outperforms state-of-the-art baselines in semantic and compositional quality while effectively preserving overall contour organization during refinement. The project page is: \href{https://yiki77.github.io/SketchDirector/}{https://yiki77.github.io/SketchDirector/}.
comment: 11 pages, 13 figures
☆ PBSR: Pseudo-Burst Super-Resolution via Zero-Shot Novel View Synthesis
Burst super-resolution (BSR) achieves higher reconstruction fidelity than single-image super-resolution (SISR) by aggregating sub-pixel information from multiple frames. However, it requires a burst of frames captured under near-static conditions, and such frames are unavailable for single photographs or archival images. We propose Pseudo-Burst Super-Resolution (PBSR), which brings BSR-style reconstruction to the single-image input setting by synthesizing pseudo-burst frames via zero-shot novel view synthesis (NVS). PBSR first generates pseudo-burst frames with realistic parallax using a viewpoint sampling strategy calibrated to the pixel-shift distribution of real burst datasets. The frames are then processed by a BSR backbone equipped with a geometry-aware alignment module that reuses the NVS camera parameters and estimated depth. While the NVS stage is zero-shot, the backbone is fine-tuned with an artifact-aware adaptation so that it can exploit the synthesized frames despite their generative artifacts. Experiments on the BurstSR dataset show that PBSR consistently improves in PSNR, SSIM, and LPIPS over the single-frame baseline with the same reconstruction backbone.
comment: Accepted for publication in New Generation Computing. 17 pages, 10 figures, 3 tables
☆ ReVEAL: Eliciting Role-Aware Latent Visual Evidence from MLLMs for Reasoning Video Object Segmentation
Reasoning video object segmentation (ReVOS) requires segmenting the target specified by an implicit query throughout a video. Existing MLLM-based methods typically require costly cross-model alignment, tying MLLM training to a downstream segmentation model. We present ReVEAL, a decoupled thinking-with-video framework for ReVOS that elicits role-aware latent visual evidence from MLLM reasoning to directly guide segmentation. ReVEAL introduces Visual Evidence Tokens (VETs) into video reasoning to associate necessary visual regions or entities with target, background, or distractor roles, while a Dynamic Attention Layer Router adaptively aggregates VET-to-video attention across layers into role-specific grounding evidence. We train the MLLM to perform VET-augmented reasoning on our constructed ReVEAL-47K dataset, with a Contrastive Attention Grounding Loss as auxiliary supervision to apply role-specific constraints to VET attention and optimize layer routing for reliable latent grounding. At inference, ReVEAL identifies the target through this thinking-with-video process and fuses the resulting role-specific evidence to guide a frozen SAM2 in predicting target masks. Experiments on five benchmarks show that ReVEAL outperforms alignment-free baselines and remains competitive with alignment-based methods.
☆ Hawkeye: Reading Illegible Text with Vision Language Models
While modern Vision-Language Models (VLMs) have been remarkably successful in general scene text recognition, they consistently struggle with the long-tail distribution of text that is degraded by motion blur or low resolution. These limitations are particularly apparent in egocentric applications like smart glasses, where text is often captured in noisy environments. In this paper, we introduce the \textbf{BSTRB} (Blurry Scene Text Recognition Benchmark), a novel benchmark of 8,398 noisy image samples with high-fidelity annotations derived from 1,699 real-world video sequences. To support this, we present \textbf{Hawkeye}, a VLM fine-tuned on BSTRB to read and reason about illegible text by leveraging contextual priors and pixel-level alignment. Our results show that \name matches much larger VLMs on BSTRB overall and exceeds them on text at or below 20 pixels, in a single pass suited to real-time deployment. We characterize the resolution at which current VLMs begin to fail.
comment: For datasets, code, models: https://byron123t.github.io/hawkeye/
☆ Coarse3D: Controllable and Faithful Sparse Structure Generation for Image-to-3D
Coarse-to-fine 3D-native generation relies heavily on the first-stage sparse structure, which determines the global orientation and overall geometry for subsequent refinement. However, existing coarse generators suffer from two key limitations: their orientation is difficult to control due to ambiguous semantic canonicalization, and their structures are often inconsistent with input images due to weak 2D-3D grounding. We propose Coarse3D, a sparse-structure generation model that produces orientation-controllable and structurally faithful voxel geometry from arbitrary unposed images. Coarse3D introduces three designs: a first-frame anchored canonical space that aligns generation with the first input view while preserving object-centric normalization; a dual-tower DiT that couples generation with auxiliary feed-forward reconstruction to learn spatially grounded features; and spatial-prior guided attention that regularizes cross-attention with pixel-to-voxel correspondence. Coarse3D can directly replace the coarse stage of existing pipelines. Experiments show that it substantially improves sparse structure quality and consistently enhances final 3D generation, achieving state-of-the-art performance.
☆ TinyAstral: Transferring Self-Evolving Harnesses to Tiny Generalist Manipulation Agents
Kehong Gong, Xiaoxu Zheng, Guanli Hou, Seunghun Lee, Mengqi Guo, Runjia Tan, Zhengyu Wen, Qi Wang, Xiaoyu He, Mingyuan Zhang, Michael Bi Mi
Robot agents built on vision-language models depend on both the planner and its harness: the tools, feedback, memory, and recovery logic around it. Can improvements to a large teacher's harness be carried into a compact agent with lower inference compute cost? We present TinyAstral, a two-stage approach. We first freeze an open-weight teacher and improve its harness through recursive self-improvement (RSI): frontier LLM agents read the teacher's trajectories and propose harness edits, and each edit is accepted only if it passes a paired statistical test. We then freeze the final harness, collect teacher trajectories with reasoning and failure recovery, and distill a small student that replaces the teacher in the same harness. On RoboTwin, harness evolution raises the teacher's success from 33.4% to 63.6%; the final 4B student reaches 62.8%. Across four harness versions, distilled 4B students score close to their corresponding teachers, and later versions yield stronger students, showing that harness evolution gains carry into compact models. The harness gains also transfer to other frozen planners without retraining. On identical episodes, the final 4B student outperforms open 4B agents, the open 27B harness-RSI model SenseNova-RoboRSI, and our own teacher model placed in three competitors' harnesses. It stays close to its teacher on all three training benchmarks, and on three benchmarks never used for RSI or training it scores close to a 552B planner running the same harness. On the measured deployment episodes, it uses about one fifth of its teacher's accelerator time.
comment: 16 pages, 5 figures. Project page: https://garfield-kh.github.io/TinyAstral/
☆ Beyond Scarcity: When Encoder Adaptation Is Worthwhile
Long-tailed recognition often learns a shared representation before correcting the classifier for class imbalance. Once the classifier has been rebalanced, however, final accuracy does not reveal whether further encoder adaptation remains worthwhile. Standard imbalance benchmarks obscure this question because they jointly vary the number of distinct examples available to rare classes and the frequency with which those examples shape the representation. We introduce a controlled evaluation framework that separates unique support from representation allocation and estimates the incremental value of encoder updates through matched classifier-only and full-network continuations from a common corrected model. The framework combines stage-specific controls, multiple representation and adaptation objectives, whole-distribution metrics, and hierarchical inference over class assignments, support reconstructions, and optimization runs. Across the controlled study, the same low-support regime moves from one in which classifier-only training is adequate to one in which encoder adaptation yields a practically meaningful benefit solely as representation allocation becomes more imbalanced. The transition persists across classifier corrections, representation objectives, normalization and augmentation controls, and ImageNet-scale evaluation, while preserving frequent-class accuracy. Under the prespecified one-percentage-point operating margin, the two regimes are separated by simultaneous confidence bounds. These results show that benchmark construction can alter a post-correction training decision and that support size alone does not characterize the remaining value of representation adaptation.
☆ From Evaluation to Evolution: Benchmarking Image Editing and Evolving Image Models
Jun He, Canmiao Fu, Ying Zhang, Zhipeng Huang, Chenyu Mu, Yazhen Xie, Chen Li, Jing Lyu, Weijia Li, Xiang Zhang
Real-world image editing requires coordinating multiple visual transformations with content preservation and reference constraints, yet aggregate scores can readily obscure unmet local requirements. Effective evaluation must therefore identify local failures and translate them into priorities for subsequent model improvement. We introduce WeGenEditBench, comprising 2,740 evaluation instances and 148 task types across single-image, text-intensive, and multi-reference editing. Pre-generated checklists shared across models decompose each request into atomic requirements; item-level deductions and visual evidence are established first and then summarized into scores for edit execution, preservation, visual quality, and task-specific correctness. To support the experience-acquisition component of recursive self-improvement (RSI), we develop WeGenEvoStudio, a unified multi-agent harness for RSI-oriented learning that integrates model-conditioned failure diagnosis, retrieval-grounded data construction, supervised adaptation, and re-evaluation. Among 15 evaluated configurations, GPT-Image-2 leads single-image editing with an Overall score of 4.076, while Seedream-5.0-Pro leads text-intensive editing at 4.291. Evolution experiments across four generation and editing benchmarks demonstrate substantial targeted gains: on their report-selected Top-5 single-image targets, FLUX.2 [dev] improves Overall from 1.083 to 3.517, while Qwen-Image-Edit-2511 gains 13.2%. Together, our benchmark and harness provide a foundation for developing more reliable image models through interpretable evaluation and feedback-driven learning.
comment: 58 pages, 42 figures
☆ Shifting Focus by Reliability: Geometric Fusion Modulation for Robust RGB-T Detection WACV 2027
RGB-thermal (RGB-T) object detectors combine visible and thermal imagery for robust detection, but signal corruption in either stream can substantially degrade accuracy. Restoration-based methods incur significant latency and do not consistently improve downstream accuracy, raising a more direct question: rather than restoring corrupted inputs, can we efficiently adapt the detector to their varying signal quality? Conventional PEFT offers an efficient route, but its input-independent corrections cannot track diverse test-time corruptions. Our controlled corruption sweeps show that corruption types have similar effects near clean conditions but diverge increasingly with severity, motivating corruption-aware, per-input adaptation. We therefore propose PG-FM (Prompt-Guided Feature Modulation), which adapts RGB-T object detectors to the estimated corruption type and severity of each modality. Under controlled RGB-T corruption simulations, PG-FM preserves clean-set performance, improves mAP50 by up to 20.8 points on FLIR and 19.9 points on KAIST, and transfers without retraining to unmodified, naturally acquired external RGB-T datasets.
comment: Accepted to WACV 2027
☆ Beyond CLIP: Calibrated (Few-Shot) Defect Detection With System One Decision Models
Cameras and vision sensors are widely used for product and process inspection in manufacturing environments. Traditionally, this process required thousands of images of both nominal and defective products to train such models. Recent research shows that vision transformer (ViT) models, such as CLIP, can reduce this requirement to 50-100 images per nominal and defective class. However, collecting even 50 defective images can be difficult in high-yield or low-volume manufacturing processes. In this paper, we examine the utility of two recently proposed structured decision models (Jev and Clef-Flash) in applications using as few as k=2 reference images per class at inference. In the process, we provide three main contributions: (a) a framework for k-shot learning of these models for image classification/inspection applications; (b) an agentic approach for automatically refining the text descriptions that guide image classification in these decision models; and (c) an approach to calibrate the resulting defect probabilities to improve their reliability for inspection decisions. We test these contributions in five manufacturing case studies. Using two reference images per class, both Jev and Clef-Flash outperform CLIP's test accuracies with the same references (0.75-1.00 [Jev] and 0.71-0.99 [Clef-Flash] vs 0.67-0.84 [CLIP]). Furthermore, we show that our agentic refinement of text descriptions significantly improves accuracy across 6 of 8 model-application combinations, and our calibration approach reduces the Brier score in 13 of the 15 model-application configurations. To support the reproducibility of our work, we provide our replication code and have created the R "decisions" package for evaluating additional models. We also provide a publicly available web app that allows practitioners to inspect their own images with no code.
comment: 10 pages, 5 figures
☆ FlowMoDE: Coarse-to-fine Flow Matching for Detail-aware Sim-to-Real Monocular Depth Estimation
Monocular depth estimation is a fundamental capability for 3D spatial perception in robotic and intelligent systems. While data-driven discriminative methods have achieved compelling performance for monocular depth estimation, generative approaches can reconstruct fine structural details with stronger generalization. However, most existing generative diffusion models suffer from low inference efficiency caused by multi-step sampling, whereas single-step diffusion models struggle to recover high-fidelity depth maps. To mitigate these drawbacks, this paper presents FlowMoDE, a coarse-to-fine flow-matching-based framework for efficient, structure-aware monocular depth estimation supporting sim-to-real and cross-scene generalization. The core architecture first conducts coarse-level decoding conditioned on diffused features extracted from a pre-trained semantic encoder, and subsequently restores fine-grained structural patterns via expanded decoding layers. Trained exclusively on a single synthetic indoor dataset, our proposed method outperforms prior methods across five real-world indoor and outdoor datasets, yielding an average 14.1% reduction in relative error against state-of-the-art works as well as 9.9\% reduction in chamfer distance for point cloud reconstruction. We further validate the generalization capability of FlowMoDE on realistic robotic scenes, with qualitative comparisons against state-of-the-art methods. The project page is https://flowmode-shao.netlify.app/
comment: 9 Pages, 7 Figures, Under Review
☆ Mechanistic Interpretability for End-to-End Self-Driving
Manav Gagvani, Benjamin Namikas, Sivamurugan Velmurugan, Nysa Kumar, Shrey Sharma, Thomas Peterson, Pratyush Mathur, Hyunseo Chang, Rohan Paleja
End-to-end (E2E) autonomous driving models achieve strong planning performance but remain largely opaque, limiting observability and trust. We apply mechanistic interpretability to E2E driving by training Sparse Autoencoders (SAEs) on the internal activations of a simple, single-camera end-to-end path planning model. Unsupervised clustering of SAE latents reveals interpretable structure aligned with route intent, vehicle speed, and braking behavior, recovering route intent with 98.3% accuracy without supervision. Through paired counterfactual image edits, we further show that specific SAE features are visually grounded and systematically respond to scene changes such as traffic light state and stop sign presence. Steering individual SAE latents produces predictable, monotonic changes in planner outputs including lateral displacement, curvature, and braking. Finally, we demonstrate a practical application of these representations: we build a lightweight adapter trained on SAE latents that reduces trajectory selection error by 3.44%, outperforming an equivalent adapter that omits SAE latents, confirming that the sparse representation captures useful information for interpretability. Code and visualizations are available at https://mgagvani.github.io/mechanistic-interpretability-for-e2e-driving/
comment: Accepted to the Conference on Robot Learning (CoRL) 2026. Project page: https://mgagvani.github.io/mechanistic-interpretability-for-e2e-driving/
☆ MATS: A Multi-Agent Framework for Controllable and Reflective Tumor Synthesis IEEE
Zhongnan Shi, Keyang Zhou, Siyi Li, Qichao Ying, Xinchen Ma, Shuyi Gong, Wei Tang, Sheng Li, Zhenxing Qian
Synthesizing realistic tumors on medical images helps address data scarcity for clinical AI. Existing diffusion-based methods, however, lack fine-grained attribute-level controllability, closed-loop quality verification, and awareness of which morphological patterns are underrepresented in the target distribution. We present MATS, a multi-agent framework for text-controllable, cross-organ tumor synthesis. Given a target disease, MATS first profiles its morphological distribution over a fine-grained attribute space covering tumor geometry and appearance, and constructs a generation plan that amplifies underrepresented subtypes while keeping common ones dominant. It then localizes the target organ, proposes anatomically valid placement, and executes two-stage diffusion to produce the tumor mask and render texture, each conditioned on the corresponding attributes. A closed-loop reflective mechanism verifies attribute alignment at three checkpoints throughout synthesis and provides targeted feedback to guide re-generation when misalignment is detected. Trained exclusively on a single-source pancreatic neuroendocrine tumor dataset, MATS consistently improves downstream segmentation both in-domain and across LiTS, KiTS, and MSD-Pancreas benchmarks, whereas most prior generative augmentation methods fall below real-only training.
comment: IEEE BIBM 2026
☆ One Verdict Is Not Enough: Regression Testing Learned World Models and Their Test Harnesses
Learned world models ship as software components whose model versions, preprocessing, and test harnesses change between releases. Work on model updates already counts per-sample regressions, or negative flips, that an accuracy check hides. Two problems remain: a test can pass under one configuration and fail under another, and a harness fault can produce erroneous scores and wrong verdicts without a crash. We present TetraState, a regression-testing protocol for paired possible/impossible video tests. Each test runs under four fixed model-native configurations; its number of correct verdicts assigns one of four outcome classes. Inconclusive tests and failed executions are reported separately alongside the verdict rate. Revisions are compared by per-test outcome transitions, and the harness is versioned as part of the system under test. On all 506 IntPhys 2 Main tests and two heterogeneous systems, Cosmos 3 Nano and V-JEPA 2, two V-JEPA 2 harness revisions move modal accuracy by only 0.2 and 2.6 points yet change 11 and 214 test labels; in the second, accuracy rises although fewer tests are judged correctly, and 43 regressions keep the modal verdict, so a modal-verdict flip check misses them. These results outline a research agenda on configuration-level regression oracles for learned components.
☆ MotorSense: A Video-EMG Dataset of Motoric Representations for Action Understanding NeurIPS 2026
Eadom Dessalene, Michael Maynord, Amir-Hossein Shahidzadeh, Botao He, Yasamin Tabatabaee, Ruoshi Liu, Ruohan Gao, Paul Pu Liang, Yiannis Aloimonos
Computer vision models learn hand action only from its visual consequences, without access to the underlying embodied motor signals. We address this absence with MotorSense, the first large-scale paired egocentric video and surface electromyography (EMG) dataset. MotorSense pairs bimanual EMG bands with egocentric video and yields synchronized video, bimanual EMG and IMU, and audio across 100 hours of unscripted manipulation. EMG offers a powerful scalable alternative to manual annotation: it captures finger movement, hand configuration, and the forces exerted between hand and environment as a dense signal free of occlusion and labeler bias, whereas manual annotation is costly, subjective, and unreliable under occlusion. We use MotorSense to learn sensorimotor alignment, producing an organization of action that is shared across the visual and motoric spaces, through cross-modal pre-training between video and EMG. The resultant vision models are then transferred to downstream tasks that benefit from sensorimotor alignment: in this case, action recognition, 3D hand reconstruction, and 3D contact reconstruction. We further demonstrate a generative application in which EMG motor signals are used to condition a video diffusion model, yielding videos of fine-grained hand manipulation. Across four action-centric computer vision tasks and eight popular video datasets, we demonstrate that sensorimotor alignment provides a stronger foundation for visual representation learning than conventional self-supervised learning methods over video, outperforming every baseline across all tasks and datasets evaluated.
comment: NeurIPS 2026. 27 pages, 9 figures, 5 tables. Project page: https://www.cs.umd.edu/~edessale/motorsense/
☆ CompoSET: A Controlled Single-Edit Benchmark for Vision-Language Compositionality
Vision-language compositionality is the ability to identify which properties, relations, quantities, and states bind to which entities in both an image and a caption, and to match those bindings across modalities. Existing benchmarks evaluate this ability through image-text matching: a model ranks candidate image-caption match similarities and succeeds when the correct match comes on top. In such datasets, paired images differ by more than a single binding: scene, layout, and content all differ, with 97% of pixels changing on average. Such pairs do not isolate compositionality: results are confounded by global image differences. We introduce Compositional Single-EdiT (CompoSET), a benchmark dataset in which each base image-caption pair has an edited counterpart, or foil, that preserves the scene but differs by a single controlled edit, such as color, count, spatial relation, or object state. Base and foil images preserve naturalistic scene complexity yet are visually near-identical: only 8% of pixels change on average. Our dataset has many foils per base scene (~22) in order to separate scene difficulty from edit difficulty, a distinction one-foil-per-base benchmarks cannot make. CompoSET contains 1,776 instances across 80 naturalistic scenes, spanning 16 edit types in 4 semantic categories, with captions at 3 verbosity levels. We test 22 dual-encoder VLMs and find that all models perform at or below chance level on CompoSET, despite scoring well on prior benchmarks. This suggests that apparent progress under prior image-text matching protocols may be an artifact arising from confounds in benchmark design, and that compositional understanding in dual-encoder VLMs is far from solved. CompoSET addresses the confounds and offers a better controlled benchmark to measure future progress.
comment: Dataset: https://huggingface.co/datasets/CompoSET/CompoSET | Code: https://github.com/laure-delisle/CompoSET | 9 pages main text, 38 pages total
☆ An Empirical Study on Learning Rooftop Solar Potential from Satellite Imagery Directly Without Explicit Geometry
Rooftop solar potential estimation often relies on LiDAR, digital surface models (DSMs), or pipelines that explicitly reconstruct roof geometry before estimating solar potential. We propose Direct Rooftop Solar Prediction (DRSP), an end-to-end formulation that maps satellite imagery and coarse regional sunlight information directly to application-level solar outputs. DRSP neither requires explicit geometric representations as input nor predicts roof orientation, slope, height, DSMs, or other geometric quantities as intermediate outputs, and therefore requires no geometric intermediate supervision. To enable large-scale study of this formulation, we develop a programmatic data-curation pipeline that converts per-panel information from the Google Solar API into usable-rooftop masks and spatial solar-energy targets, yielding two geographically distinct datasets containing approximately 60K rooftops from the U.S. Midwest and 87K from Texas. We demonstrate the feasibility of DRSP and systematically characterize the effects of geographic training distribution, model depth, image resolution, regional sunlight conditioning, and heatmap regression objectives. Overall, our results suggest that useful rooftop-scale solar information can be learned directly from 2D imagery without explicit geometric intermediate representations, offering a simpler path toward large-scale solar assessment. Our code and dataset are available at https://github.com/joshualee124/direct_rooftop_solar_prediction.
♻ ☆ SUREON: A Benchmark and Vision-Language Model for Surgical Reasoning WACV 2027
Alejandra Perez, Anita Rau, Busisiwe Mlambo, Lee White, Aina Brunet, Juan Verde, Juan Caicedo, Joël L. Lavanchy, Adam Schmidt, Chinedu Nwoye, Omid Mohareri, Muhammad Abdullah Jamal
A capable surgical AI model should reason from visual evidence; yet, many existing surgical benchmarks can be partially solved without visual input, as models may exploit procedural priors or common anatomy-instrument interactions to infer answers from language alone. To address this, we introduce SUREON, a video question-answering (QA) dataset and benchmark designed to evaluate visually grounded surgical reasoning. Our pipeline generates QA pairs from narrated surgical academic videos, refines incorrect options using multiple models to reduce linguistic biases, and uses trained adversarial guessers to filter out questions answerable without visual evidence. The resulting dataset contains 76K training pairs covering 168 procedures and 10 specialties, alongside a benchmark of 340 questions across 10 categories verified by expert surgeons. Benchmarking leading commercial and open-source models shows that surgical visual reasoning remains largely unsolved--no model exceeds 53% accuracy and even expert surgeons reach only 64.9%. We also introduce SUREON-VLM, a baseline trained on our curated data that avoids language shortcuts, outperforms existing open-source models on SUREON, and matches larger specialist models on multiple standard surgical benchmarks. Our training ablations show that distractor refinement and blind curation strengthen visual grounding, increasing the multimodal gain from 8.7 to 29.1 percentage points while reducing the training set from 176.6K to 76K filtered samples. SUREON thus offers both a verified testbed for surgical visual reasoning and training data that strengthens visual grounding.
comment: WACV 2027
♻ ☆ VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL
Yichen Feng, Zhangchen Xu, Fengqing Jiang, Yuetai Li, Bhaskar Ramasubramanian, Luyao Niu, Bill Yuchen Lin, Radha Poovendran
Vision language models (VLMs) are expected to perform effective multimodal reasoning and make logically coherent decisions, which is critical to tasks such as diagram understanding and spatial problem solving. However, current VLM reasoning lacks large-scale and well-structured training datasets. To bridge this gap, we propose VisualSphinx, a first-of-its-kind large-scale synthetic visual logical reasoning training dataset. To tackle the challenge of image synthesis with grounded answers, we propose a rule-to-image synthesis pipeline that extracts and expands puzzle rules from seed questions and generates executable rendering programs for puzzle assembly. Using standard GRPO with binary correctness rewards as a validation recipe, experiments show that VLMs trained on VisualSphinx benefit from the logical coherence and readability of our dataset and exhibit improved performance on logical reasoning tasks. The enhanced reasoning capabilities developed from VisualSphinx also benefit other reasoning tasks such as algebraic reasoning, arithmetic reasoning and geometry reasoning.
comment: DMLR; Project page at https://visualsphinx.github.io/
♻ ☆ Integrating AI for Human-Centric Breast Cancer Diagnostics: A Multi-Scale and Multi-View Swin Transformer Framework
Despite advancements in Computer-Aided Diagnosis (CAD) systems, breast cancer remains one of the leading causes of cancer-related deaths among women worldwide. Recent breakthroughs in Artificial Intelligence (AI) have shown significant promise in development of advanced Deep Learning (DL) architectures for breast cancer diagnosis through mammography. In this context, the paper focuses on the integration of AI within a Human-Centric workflow to enhance breast cancer diagnostics. Key challenges are, however, largely overlooked such as reliance on detailed tumor annotations and susceptibility to missing views, particularly during test time. To address these issues, we propose a hybrid, multi-scale and multi-view Swin Transformer-based framework (MSMV-Swin) that enhances diagnostic robustness and accuracy. The proposed MSMV-Swin framework is designed to work as a decision-support tool, helping radiologists analyze multi-view mammograms more effectively. More specifically, the MSMV-Swin framework leverages the Segment Anything Model (SAM) to isolate the breast lobe, reducing background noise and enabling comprehensive feature extraction. The multi-scale nature of the proposed MSMV-Swin framework accounts for tumor-specific regions as well as the spatial characteristics of tissues surrounding the tumor, capturing both localized and contextual information. The integration of contextual and localized data ensures that MSMV-Swin's outputs align with the way radiologists interpret mammograms, fostering better human-AI interaction and trust. A hybrid fusion structure is then designed to ensure robustness against missing views, a common occurrence in clinical practice when only a single mammogram view is available.
♻ ☆ An Agentic AI Workflow for Liver Lesion Segmentation and Structured Radiology Reporting
Despite advances in automated liver lesion segmentation, a gap remains between image-derived predictions and clinically interpretable radiology reports. Bridging this gap requires quantitative measurements that are both reproducible and traceable. We present an evidence-grounded agentic AI framework that connects liver and lesion segmentation with deterministic quantification and structured reporting. TotalSegmentator-based nnU-Net models delineate the liver, Couinaud segments, and lesions, while computational tools extract volumes, dimensions, counts, and anatomical locations. A large language model (LLM) organizes these measurements into a predefined reporting schema, with numerical consistency checks against source measurements. On 51 LiTS cases, the segmentation stage achieved a liver Dice score of 0.947, lesion sensitivity of 75.6%, and positive predictive value of 68.9%. Exploratory screening of 25,494 MERLIN reports identified 10,208 (40.0%) with liver-related abnormalities. By connecting quantitative imaging evidence with agentic report generation, this work takes a step toward human-centered medical AI that prioritizes traceability and clinical relevance.
♻ ☆ Sounding Surgical Instrument Localisation through Audio-Visual Correspondence
Surgical scene understanding is a prerequisite for context-aware and intelligent systems in the operating room. Current methods rely mostly on visual data, although sound carries complementary information on surgical activity. This work investigates whether the active instrument can be localised from a single microphone and a camera by learning the correspondence between the appearance and sound of surgical instruments. We adapt weakly supervised audio-visual localisation, which learns from the co-occurrence of image and sound, to the surgical setting. Trained without any spatial annotation, the model predicts a heatmap over the sounding instrument. We extend its training objective by silence and area terms, which suppress the output for silence and background noise, and a composite term, which trains the model to select the sounding one of two visible instruments. For training, we record a new corpus of simulated surgical sawing, drilling, and chiselling. On an independent corpus recorded with a different camera and microphone and with optically tracked instrument positions, the model localises the active instrument, separates instrument activity from silence and background noise, and attributes sound correctly when two instruments are present. Ablations show that the silence and area terms are required for detecting activity, and the composite term for selecting between instruments. Audio-visual correspondence learned from in-domain recordings connects the sound of a surgical action to the instrument that produces it. This proof of concept introduces a novel paradigm that enables the detection and localisation of surgical activity without annotating instrument positions.
♻ ☆ Generalizable Multiscale Segmentation of Heterogeneous Map Collections
Historical map collections are highly diverse in style, scale, and geographic focus, often consisting of many single-sheet documents. Yet most work in map recognition focuses on specialist models tailored to homogeneous map series. In contrast, this article aims to develop generalizable semantic segmentation models and a shared class ontology. We introduce Semap, a new open benchmark dataset comprising 1,439 manually annotated patches designed to reflect the variety of historical map documents. We also evaluate a segmentation workflow that combines procedural data synthesis with multiscale integration. Under the harmonized benchmarks considered here, this workflow attains the strongest performance among the evaluated approaches and viably learns from heterogeneous map data. The results indicate that segmentation performance remains largely stable across map collections, scales, geographic regions, and publication contexts. By providing benchmark data, a shared ontology, and an evaluation of generic segmentation across heterogeneous maps, this work contributes toward integrating the long tail of cartographic archives into historical geographic studies.
comment: 33 pages, 16 figures
♻ ☆ Analyzing the Optimization Conflict Between Image- and Text-Based Person Re-Identification
Unifying image-based (I2I) and text-based (T2I) person re-identification (ReID) within a single model is complicated by a fundamental conflict between their training objectives. While I2I ReID focuses on identity-level invariance across images of the same person captured by multiple cameras, T2I ReID is driven by instance-specific textual descriptions tied to unique visual traits. When both objectives are optimized on a shared vision encoder, they impose opposing requirements on the same embeddings - I2I contracts intra-identity distances while T2I requires them to persist - so improving one task's representation actively degrades the other's. This paper analyzes this optimization conflict and shows empirically that the two objectives are not jointly satisfiable by a single unconstrained encoder. Motivated by this finding, we investigate the conflict through a controlled two-stage pipeline: a shared vision encoder is first trained for I2I invariance, then frozen while only the text encoder is aligned to the fixed visual space, preventing the T2I objective from unwinding the identity-level structure already learned. We conduct extensive experiments across multiple configurations, varying domain-mixing procedures, learning strategies, and task objectives. We observe that I2I pre-training improves subsequent T2I performance, while textual supervision during this pre-training further improves T2I performance, although the vision encoder is frozen during subsequent cross-modal training. We believe these insights provide a meaningful step toward unified ReID systems that reconcile identity-level and instance-level supervision within a single representation.
♻ ☆ Frequency-Adaptive Gaussian Grids for Efficient Image Representation
Neural representation has emerged as a powerful tool to mitigate the demand for increasingly higher-resolution images. However, a fundamental trade-off remains between structured (e.g., feature grids, which offer spatial consistency) and unstructured (e.g., 2D Gaussians, which adaptively capture local variances) representations. This choice significantly affects how well an algorithm represents an image. Here, we present Gaussian-Grid, a hybrid neural representation that adaptively integrates structured and unstructured primitives. By dynamically partitioning computation between 2D Gaussian distributions and their attached multi-resolution feature grids based on the local frequency content, Gaussian-Grid automatically optimizes its architecture to match the underlying complexity of the target signal. We evaluate our approach using high-resolution natural images and demonstrate its applications as GPU-native dataset storage, showing $1.24$-$2.14\times$ decoding speed improvement and PCIe data traffic reduction of $23\times$-$176\times$ over JPEG + ZIP data archiving. The results show that Gaussian-Grid maintains small memory footprints while achieving high reconstruction fidelity across a variety of datasets.
♻ ☆ Understanding the Performance Plateau in Text-to-Video Retrieval: A Comprehensive Empirical and Linguistic Analysis
Maria-Eirini Pegia, Dimitrios Stefanopoulos, Björn Þór Jónsson, Anastasia Moumtzidou, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris
Text-to-video retrieval enables users to find relevant video content using natural language queries, a task that has grown increasingly important with the rapid expansion of online video. Over the past six years, research has produced numerous methods, such as dual encoders, attention-driven models, and multimodal fusion approaches; however, fundamental questions remain about model behavior, dataset influence, and query difficulty. In this work, we evaluate 14 state-of-the-art retrieval methods across 3 widely used datasets under a unified preprocessing and evaluation framework. We analyze caption characteristics, including length, clarity, semantic category, and Action vs. Scene balance, and link these to model performance. Our results show that short, clear, and simple captions, such as those describing single actions or color attributes, achieve higher recall, while complex events, multi-step activities, or fine-grained scene descriptions remain challenging for all existing models. Attention-driven architectures better handle temporally dependent or multi-step queries, whereas dual-encoder and multimodal fusion models perform well primarily on simpler or single-category captions. Cross-dataset generalization improves with larger, more diverse caption sets, but generative captions do not consistently enhance retrieval accuracy. Overall, our findings highlight key dataset factors, benchmark challenges, and the interplay between query content and model architecture, providing guidance for developing more effective text-to-video retrieval systems.
comment: Survey, 50 pages, 15 figures, 13 tables, 154 citations
♻ ☆ GROME: GROunding Factorized MEmory into Video World Models
Fang Yu, Xiaoxue Wu, Haiyu Zhang, Yangfu Li, Botong Zhao, Yu Qiao, Tianfan Xue, Yue Lu, Xinyuan Chen
Long-horizon consistency is essential for video world models, yet it becomes difficult to maintain once observed content falls outside the context window. Existing memory mechanisms either retrieve history as implicit context, which remains constrained by context capacity and entangles static and dynamic information or consolidate history into explicit global representations, which struggle to model evolving dynamics and accumulate geometric errors. To overcome these limitations, we introduce GROME, a novel factorized memory framework that augments video world models for long-horizon coherence across both static scenes and dynamic entities. GROME operates via three synergistic components: 1) Static-Dynamic Factorization decouples historical evidence into static and dynamic components, preserving static scene geometry and dynamic entity states separately; 2) Local Memory Retrieval selects spatially aligned static history to eliminate geometric drift alongside the latest dynamic entity states to ensure temporal continuity; 3) Training-Free Memory Injection seamlessly grounds the factorized memory into video world models via their native conditioning pathways. Experiments on long-horizon revisit scenarios demonstrate that GROME substantially enhances scene persistence and dynamic entity fidelity over existing methods, all while preserving the visual quality and controllability of the frozen backbone without additional training. Beyond self-rollouts, GROME readily accommodates external observations as plug-and-play memory, unlocking flexible and controllable video continuation and scene exploration.
♻ ☆ Vision Transformer Ensembles for Panoramic Street Segmentation
Semantic segmentation of street panoramas can support detailed descriptions of urban environments, yet small datasets and unequal training costs make model selection difficult. This paper documents the two stages of a submission to the PalmCity challenge. In the first stage, nine pretrained segmentation systems are compared using approximately equal computation budgets, and the two leading candidates are trained with three random seeds. An equal probability average of three Encoder only Mask Transformer models with DINOv3 features, evaluated at three image scales with horizontal reflection, reaches 60.95% mean intersection over union on the 84 image validation split and 57.08% on the hidden test leaderboard, first in the snapshot of 5 October 2026. In the second stage, color and crop augmentation, rare class sampling, larger inputs, further architectures, and post processing are tested under the same update budget, and candidates are compared through a paired image bootstrap with selection rules fixed before the results. Training at 768 by 1536 and 896 by 1792 pixels gives the largest gains. The selected ensemble of four models scores 64.53% on validation when trained on the training images alone. Retrained on the training and validation images, it receives 61.54% mean intersection over union and 71.93% mean F1 on the hidden test leaderboard, first among five entries in the snapshot of 30 October 2026. Segment Anything voting, instance mask overlays, and ensembles with other architectures all reduce the validation score. Because the validation images guided many choices and the final models were trained on them, the validation scores are optimistic and the test score is the only independent measurement. The study reports all runs, comparisons, costs, and limits, and releases the code and models.
comment: 26 pages, 6 figures, 12 tables. Version 2 adds the second stage of 28 to 30 October 2026 with its hidden test result, negative results and limits
♻ ☆ Sexual dimorphism and sex-specific features of pediatric hand bone shapes via statistical shape modeling
Understanding the envelope of shape variation encompassing both sexual dimorphism and growth-related changes in living tissue is a non-trivial task. In this work, we use statistical shape models (SSMs) and analyze demographic-stratified shape parameters to quantify sexual dimorphism and sex-specific shape changes. To this end, we developed a two-stage detection-segmentation framework that accurately segmented $n = 27664$ unique bone masks from a radiographic dataset of $n = 1456$ subjects with diverse ages and sexes (HD95 = $1.512 \pm 0.005$, ASSD $0.449 \pm 001$, Dice = $0.987 \pm 0.00004$, IoU = $0.975 \pm 0.0001$). Following SSM fitting, our follow-up analyses of stratified shape parameters highlighted the morphology of sexual dimorphisms for each bone and demonstrated sex-specific differences in shape development. Our results were consistent with existing literature and provided an unambiguous, data-driven methodology for characterizing tissue shape and its population-scale variation. This approach provides a potential methodological foundation for developing population-based models of anatomical variation and could be extended to investigate tissue growth and shape changes over time. Future work should evaluate the applicability of this methodology on alternative anatomical structures and imaging modalities, with the goal of validating these growth trajectories on longitudinal data.
♻ ☆ DAUPNet: Domain-Aware Uncertainty Modeling for Reliable Prototype Discrimination in Cross-Domain Few-Shot Semantic Segmentation
Cross-domain few-shot semantic segmentation (CD-FSS) has predominantly been formulated as learning domain-invariant representations or improving support-query correspondence. Nevertheless, large domain shifts still make prototype matching unreliable: inconsistent hierarchical responses corrupt the support representation, deterministic prototypes cannot express boundary and appearance ambiguity, and treating prototypes with different reliability equally during optimization weakens foreground-background separation. We therefore propose DAUPNet, a unified framework that reformulates cross-domain prototype matching as uncertainty-aware prototype discrimination. DAUPNet first harmonizes hierarchical support-query features to provide stable evidence, then represents foreground and background prototypes probabilistically, and finally uses their estimated uncertainty to regulate contrastive optimization. On four standard target domains, DAUPNet achieves 72.6% and 76.7% average mIoU in the 1-shot and 5-shot settings, respectively, including substantial gains on the two medical domains. These results demonstrate that modeling prototype uncertainty and incorporating it into optimization provides a robust and interpretable approach to CD-FSS under severe domain shift. The code is available at https://github.com/madness-Lei/DAUPNet
♻ ☆ Open-Vocabulary Models and Label Propagation for Uncertainty-aware Pseudo-Labeling of Object Detection Datasets
Object detection models rely on large-scale annotated datasets that require substantial manual annotation effort. The labeling costs further increase when annotation uncertainty is captured through soft labels, as multiple annotations must be collected for each object. We address this challenge by introducing a semi-supervised framework for the uncertainty-aware annotation of object detection datasets. Our approach leverages open-vocabulary object detectors to generate region proposals and semantically meaningful object representations. Based on these representations, we construct an object-level similarity graph and propagate probabilistic label information. Our framework assigns soft labels to previously unlabeled objects, quantifying both aleatoric and epistemic uncertainty. We evaluate our method on four object detection benchmarks with soft-label annotations and demonstrate that high-quality annotations can be obtained with only a small fraction of manually labeled objects. Furthermore, we show that detectors trained on semi-automatically annotated data achieve performance comparable to models trained on fully manually annotated data. We achieve state-of-the-art downstream detection performance for the task of semi-supervised object detection on the COCO dataset, with 40.5% AP virtually matching the performance of the model trained on fully manually constructed data (40.9%) at only 1% of labeling costs. These results highlight the potential of open-vocabulary models and graph-based label propagation for efficient, accurate and uncertainty-aware pseudo-labeling.
♻ ☆ Satellite-derived bathymetry of Arabian Gulf ports under leakage-free validation: a multi-site benchmark of empirical, machine-learning and deep-learning models against 285,000 multibeam soundings, with an outlook for UAE waters
Satellite-derived bathymetry (SDB) is often proposed as a low-cost complement to multibeam surveys of ports, but its accuracy is seldom measured on an area the model has never seen. We test SDB in the turbid, shallow and tidal waters of the Abu Dhabi coast against 285,619 multibeam soundings from twelve surveys at six port areas, reduced to Lowest Astronomical Tide. The Stumpf ratio, the Lyzenga transform, a monotonic gradient-boosted tree model (LightGBM) and a U-Net ensemble were trained on 72 Sentinel-2 images and tested in transfer to an unseen site and with local control (about 20% of a site's soundings, spatial blocks beyond a 225 m buffer). The validation design changed the result more than the model: the same LightGBM model gave 0.57 m RMSE under a random pixel split, 1.30 m under spatial blocks and 1.66 m at an unseen site. In transfer the Stumpf ratio (1.45 m) was not beaten by LightGBM, the U-Net or Lyzenga. With local control a hybrid of the tree output and the band ratio reached 1.11 m over 0-11 m, 67% of pixels within the IHO S-44 Order 2 tolerance and a conformal 95% interval with 95% observed coverage; in clear water it reached 0.29-0.53 m. The seabed could not be read beyond 11 m, and summer images were five to six times more turbid than winter ones. ICESat-2 ATL24 agreed with the surveys to 0.25 m after a depth-dependent offset was removed. A 60 m reconnaissance mosaic of 17,200 km2 of the UAE coast, calibrated cell by cell on ICESat-2 without any vessel survey, reached 1.42 m RMSE on held-out blocks over half of the product. Sentinel-2 SDB with about a hundred control depths per image is a usable reconnaissance and change-monitoring tool in these waters to about 10 m; it does not meet S-44 Order 1 without local control, and accuracies from random splits should not be taken as operational accuracy.
comment: 54 pages, 28 figures, 25 tables. v2: author list revised; no change to the scientific content
♻ ☆ Soft Mixture-of-Recursions: Going Deeper with Recursive Vision Transformers WACV 2027
Vision Transformers are typically scaled by increasing depth or width, at the cost of more unique parameters. We explore recursion as an alternative scaling axis that increases effective computational depth through parameter reuse. Yet simply increasing recursion depth does not reliably improve performance because representations from different recursion stages are not fully utilized. We propose Soft Mixture-of-Recursions (SoftMoR), which learns token-wise weights to combine representations across recursion steps, and instantiate it as the Soft Recursive Vision Transformer (SR-ViT). Experiments on ImageNet-1K, COCO, and ADE20K show that SR-ViT can benefit from deeper recursion while adding few parameters. On ImageNet-1K, increasing recursion depth from T=1 to T=3 improves top-1 accuracy from 79.83% to 82.45% with only 1.1M additional parameters. At matched effective depth, SR-ViT also outperforms independently parameterized counterparts with substantially fewer parameters. These results support recursive deepening as an effective, parameter-efficient scaling axis for Vision Transformers. The code is available at https://github.com/sangin-lee/sr-vit .
comment: Accepted to WACV 2027. 17 pages, 8 figures
♻ ☆ SV-WAM: An Efficient Surround-View World-Action Model for End-to-End Autonomous Driving
Jinyang Wang, Shiwei Li, Junjian Wang, Zhiqiang Deng, Jianbin Gao, Yihang Zhao, Liu Liu, Yongjia Zhao, Jinlong Chen, Huirui Xu, Yifeng Pan, Kangwei Liu, Fan Ren, Ji Tao, Minghao Yang
World models (WMs) have demonstrated strong potential for end-to-end autonomous driving by learning predictive representations of future scene dynamics. However, generating future videos during inference introduces substantial computational overhead, leading many recent driving WMs to adopt a single front camera as input for efficient deployment. This design restricts spatial coverage in safety-critical maneuvers such as lane changes, merges, and turns. To address this limitation, we propose SV-WAM, a surround-view world-action model (WAM) that preserves full six-camera observations while maintaining efficient inference. SV-WAM leverages future-video prediction as dense training supervision for action learning within a shared generative model, rather than as an inference-time output. At the core of this design is an action-centered causal mask that prevents action tokens from attending to future-video tokens during joint action-video denoising. Consequently, the video branch can be discarded at deployment, enabling efficient action-only planning. Furthermore, we introduce a differentiable drivable-area compliance regularizer that penalizes vehicle-footprint corners approaching or crossing drivable boundaries, improving planning safety and boundary awareness. Extensive experiments on the closed-loop NAVSIMv2 benchmark and the open-loop nuScenes benchmark demonstrate that SV-WAM achieves state-of-the-art planning performance with low inference latency and competitive zero-shot transfer capability.
comment: 23 pages, 16 figures. Project page: https://jinyangwang427.github.io/SV-WAM/
♻ ☆ HOPE: Hand-Object Pressure Estimation from Monocular Videos
Estimating physical pressure from vision is essential for understanding contact-rich hand-object interaction. However, prior vision-based pressure estimation methods are largely limited to planar surfaces and single image input, making them difficult to apply to dynamic hand-object interaction with diverse objects. We instead formulate pressure estimation as a hand-centric video prediction problem with monocular video as input. This formulation predicts temporally evolving per-vertex normal pressure and contact directly on the hand mesh, yielding a unified output space independent of object shape and sensor layout. Building on this formulation, we propose \textbf{HOPE}, a framework with two key components. First, we lift tactile-glove pressure, planar-sensor pressure, and distance-based hand-object contact annotations into a shared hand vertex space, allowing bare-hand contact data to regularize pressure learning where labels are unavailable. Second, we introduce a vertex-anchored video transformer that treats each vertex as a persistent token, aggregates visual features and hand pose over time, and uses a contact-gated pressure head to suppress pressure predictions in regions with low contact probability. Experiments on OpenTouch, PressureVisionDB, and hand-object contact benchmarks validate HOPE across object-pressure, surface-pressure, and contact-supervised HOI settings. Despite using pressure supervision primarily from gloved-hand videos, HOPE generalizes to bare-hand egocentric and in-the-wild videos, producing joint contact and pressure predictions beyond the scope of contact-only or planar-pressure baselines.
comment: project page is at: https://subin6.github.io/page-hope
♻ ☆ NanoVSR: Towards Real-Time Video Super-Resolution on Edge Devices ECCV 2026
Recent Video Super-Resolution (VSR) methods rely heavily on transformers and explicit optical flow, creating computational overhead and custom operations that hinder deployment on hardware accelerators like TensorRT. To address this, we introduce NanoVSR, a scalable, fully convolutional architecture designed for resource-constrained edge devices. Using structural reparameterization, NanoVSR collapses into standard convolutions during inference, ensuring seamless hardware compatibility and negligible runtime overhead. Furthermore, despite lacking explicit motion compensation, it maintains competitive restoration quality by implicitly learning spatio-temporal alignments through progressive training. Evaluated on the REDS4 benchmark, NanoVSR demonstrates an exceptional balance between accuracy and computational efficiency, significantly improving the trade-off for compact architectures. Our NanoVSR-644k baseline yields 28.64 dB PSNR while delivering 27.2 FPS on the NVIDIA Jetson Orin NX 16GB (25W), offering massive speed gains over heavier models. The scaled NanoVSR-1.7M variant reaches 29.15 dB with a throughput of 19.58 FPS, providing superior, edge-optimized upscaling. Code is available at https://github.com/filippawlicki/nanovsr.
comment: Final version published at ECCV 2026
♻ ☆ Sub-Voxel Displacement and Ipsilateral Territory Decoupling for 3D Bilateral Coronary Artery Tree Reconstruction in Cardiac CT Angiography
Bilateral coronary tree (LCA/RCA) reconstruction in cardiac CT angiography (CCTA) faces $64^3$ discretization, territory ambiguity, and ostium drift. Within GT-foreground $128^3$ ROIs ($\pm 15$-voxel margin, 9.8--12.0 mm; seed-free in ROI; whole-volume localization upstream), we propose a canonical $(Z_{IS},Y_{PA},X_{LR})$ multi-task network and scalar-potential arborescence solver. Volumes, centerlines, and thick-stem ostia map to $(Z_{IS},Y_{PA},X_{LR})$, extending a 3D pyramid encoder and tubular decoder with a $128^3\to 64^3$ skip, stop-gradient $128^3$ residual branch, 3D sub-voxel displacement head $Δ\hat{u}(x)$, ipsilateral head $(P_{LCA},P_{RCA})$, and $32^3$ ostium head. From a recall-balanced anchor, 327,385 active parameters (398,843 total) are fine-tuned in a 30-epoch AdamW loop ($ε=10^{-8}$, 7 groups) with L2-SP/anchor blending. Our solver couples Valsalva-STJ calibration, $128^3$ rooting, contralateral inhibition, radius-ratio pruning, normal-plane pullback, and collision reduction. On OOS-50 (Cases 251--300, 50 volumes, 100 trees; 201--250 isolated) vs. 3D U-Net (skeleton/$M_3$--$M_5$) and 5 proxies, it reaches $2.7671\pm 1.1385$ mm $CD_{CL}^{union}$ (SD 0.0415), $3.2338\pm 1.3557$ mm $CD_{CL}^{arc1mm}$, and $3.2926\pm 2.6253$ mm $CD_{ipsi}$ (arc1mm 3.7460 mm, node $p_{Holm}\le 0.0400$ vs. Ext-0b [arc1mm $p=0.1629/0.2187$]; 76.33% junction [23.04% 1-to-1], 79.65% branch F1). On Dev-14 (14 volumes, 28 trees), it reaches $2.1362\pm 0.5041$ mm $CD_{CL}^{union}$ (SD 0.0369), $2.3798\pm 0.5725$ mm $CD_{CL}^{arc1mm}$, and $2.2480\pm 0.6835$ mm $CD_{ipsi}$ (arc1mm 2.4951 mm, node $CD_{CL}^{union}$ $p_{Holm}=0.0494$ vs. Ext-0b [$CD_{ipsi}$/arc1mm $p_{Holm}=0.1353/0.0676$--$0.0906$]; 56.76%/77.74% F1@1/2mm; 79.95% junction [21.06% 1-to-1], 83.80% branch F1, 4.6880/4.9298 mm ostia), and $N=64$ $CD_{CL}^{union}=2.6291\pm 1.0629$ mm (arc1mm 3.0470, $CD_{ipsi}$ 3.0641).
comment: 12 pages (8-page main manuscript and 4-page supplementary material), 2 figures, 8 tables, 5 algorithms
♻ ☆ WildOS: Open-Vocabulary Object Search in the Wild IEEE
Hardik Shah, Manthan Patel, Erica Tevere, Deegan Atha, Marcel Kaufmann, Shehryar Khattak, Marco Hutter, Jonas Frey, Patrick Spieler
Autonomous navigation in complex, unstructured outdoor environments requires robots to operate over long ranges without prior maps and limited depth sensing. In such settings, relying solely on geometric frontiers for exploration is often insufficient. In such settings, the ability to reason semantically about where to go and what is safe to traverse is crucial for robust, efficient exploration. This work presents WildOS, a unified system for long-range, open-vocabulary object search that combines safe geometric exploration with semantic visual reasoning. WildOS builds a sparse navigation graph to maintain spatial memory, while utilizing a foundation-model-based vision module, ExploRFM, to score frontier nodes of the graph. ExploRFM simultaneously predicts traversability, visual frontiers, and object similarity in image space, enabling real-time, onboard semantic navigation tasks. The resulting vision-scored graph enables the robot to explore semantically meaningful directions while ensuring geometric safety. Furthermore, we introduce a particle-filter-based method for coarse localization of the open-vocabulary target query, that estimates candidate goal positions beyond the robot's immediate depth horizon, enabling effective planning toward distant goals. Extensive closed-loop field experiments across diverse off-road and urban terrains demonstrate that WildOS enables robust navigation, significantly outperforming purely geometric and purely vision-based baselines in both efficiency and autonomy. Our results highlight the potential of vision foundation models to drive open-world robotic behaviors that are both semantically informed and geometrically grounded. Project Page: https://leggedrobotics.github.io/wildos/
comment: Published at IEEE Transactions on Field Robotics
♻ ☆ Differential Vector Erasure: Unified Training-Free Concept Erasure for Flow Matching Models NeurIPS 2026
Text-to-image diffusion models have demonstrated remarkable capabilities in generating high-quality images, yet their tendency to reproduce undesirable concepts, such as NSFW content, copyrighted styles, or specific objects, poses growing concerns for safe and controllable deployment. While existing concept erasure approaches primarily focus on DDPM-based diffusion models and rely on costly fine-tuning, the recent emergence of flow matching models introduces a fundamentally different generative paradigm for which prior methods are not directly applicable. In this paper, we propose Differential Vector Erasure (DVE), a training-free concept erasure method specifically designed for flow matching models. Our key insight is that semantic concepts are implicitly encoded in the directional structure of the velocity field governing the generative flow. Leveraging this observation, we construct a differential vector field that characterizes the directional discrepancy between a target concept and a carefully chosen anchor concept. During inference, DVE selectively removes concept-specific components by projecting the velocity field onto the differential direction, enabling precise concept suppression without affecting irrelevant semantics. Extensive experiments on FLUX demonstrate that DVE consistently outperforms existing baselines on a wide range of concept erasure tasks, including NSFW suppression, artistic style removal, and object erasure, while preserving image quality and diversity. Code is available at https://github.com/zhiqizhang23/DVE.
comment: Accepted to NeurIPS 2026
♻ ☆ AME: A Multi-Type Contributor Attribution Framework in Generative AI Markets
Generative AI enables value creation through multi-stage collaboration among heterogeneous contributors, including training data, base models, fine-tuning behaviors, and prompts. However, how to fairly allocate the data value remains largely unexplored. This paper formulates multi-stage generative AI value allocation as a new research problem and identifies three core challenges: heterogeneous data contribution valuation, data rights mapping, and trustworthy execution. We propose AME (Attribution-Mapping-Execution) framework, a unified framework that integrates data contribution valuation, data rights mapping, and trustworthy execution into a single workflow. Experimental results demonstrate that AME framework achieves data value allocation outcomes more consistent with human reference judgments while maintaining low-cost trustworthy execution. Our work provides an initial foundation for value assessment and revenue allocation in generative AI data markets.
comment: We have decided to withdraw this manuscript because the research has undergone substantial revisions in its problem formulation, scope, and evaluation. As a result, the current version no longer adequately represents the revised study. We therefore withdraw this earlier version to avoid potential misunderstandings regarding the research findings
♻ ☆ A Lightweight Fiducial-Based Pipeline for 3D Hyperspectral Mapping of ex-vivo Lumpectomy Specimens
Anna Bicchi, Alberto Rota, Leonardo Passoni, Nicola Ancellotti, Andrea Peroni, Lorenzo Vinco, Andrea Rabolini, Arianna Bonizzi, Carlo Francesco Morasso, Fabrizio Preda, Renzo Vanna, Dario Polli, Elena De Momi
Hyperspectral Imaging (HSI) is a promising modality for intraoperative assessment of resection margins in Breast-Conserving Surgery (BCS), but its clinical translation requires aligning the inherently 2D spectral information onto the 3D shape of the excised tissue so that suspicious regions can be precisely localized for targeted follow-up. We present a fully automated, calibration-free pipeline that produces a 3D hyperspectral point cloud of an ex-vivo lumpectomy specimen from a set of consumer-camera RGB images and a single top-down HSI acquisition. The 3D geometry is reconstructed with a deep-learning Structure-from-Motion backbone, stabilized in a metric reference frame by a custom bundle adjustment that enforces consistency on the corners of four ArUco markers placed around the specimen. The HSI cube is then registered to the reconstruction without recovering the HSI camera pose: the markers, visible in both modalities, define 16 corner correspondences that drive a planar homography, and 3D coordinates are recovered by lookup on an orthographically rendered depth map. Evaluated on two ex-vivo lumpectomy specimens, the pipeline achieves a median 3D registration error below 1 mm and a 2D reprojection error below 0.02 mm, with a total per-specimen processing time under 4 minutes on accelerated hardware. These results support the feasibility of integrating HSI-guided spatial localization into intraoperative margin assessment workflows for breast-conserving surgery.
♻ ☆ Seeing and Imagining as One: Unified Video Generation and 4D Perception for Embodied World Modeling
Haitang Feng, Zhuoman Liu, Zhennan Wang, Sheng Wang, Juelin Li, Chengzhi Zhao, Hexiang Wei, Lin Shao
Embodied world models should not only imagine plausible futures, but also perceive the geometric, semantic, and motion structure of the evolving world. We present GenSight, a unified embodied world model that brings future video generation and dense perception into a single pretrained video diffusion transformer. GenSight uses a unified spatiotemporal representation for video, depth, embodiment segmentation, and first-frame-relative 2D flow. Built on a shared DiT backbone, GenSight uses lightweight task modulation to predict all modalities within the same architecture. All tasks are jointly optimized within a unified velocity prediction space. At inference, GenSight supports two complementary modes: multi-step future video generation with aligned dense predictions and single-step dense perception from an observed video. By unifying modality representation, model architecture, and prediction space, GenSight avoids separate modality-specific pipelines and couples appearance, geometry, semantics, and motion in a shared representation. It promotes cross-modal consistency and allows dense perception to benefit from the spatiotemporal priors learned through video generation. Trained on embodied manipulation data spanning multiple robot embodiments and human-hand interactions, GenSight demonstrates strong performance in both video generation and dense 4D perception, highlighting the effectiveness of unifying seeing and imagining for embodied world modeling.
comment: Project page: https://gensight-rbs.github.io/
♻ ☆ Position: Next-Generation Game Engines Should Be Built on Interactive Generative Video NeurIPS 2026
Modern game development faces significant challenges in creativity and cost due to predetermined content in traditional game engines. Recent breakthroughs in video generation models, capable of synthesizing realistic and interactive virtual environments, present an opportunity to revolutionize game creation. This position paper argues that next-generation game engines should be built on Interactive Generative Video (IGV). We term such engines Generative Game Engines (GGE), with IGV as the perceptual and world-generation core under designer control. We argue why IGV is uniquely suited for this role, identify what is missing, and examine progress across six functional modules to show how each supports our position, culminating in a five-level maturity roadmap (L0-L4). Drawing on discussions with researchers, game developers, and community participants, we present nine alternative views and ethical considerations to stimulate debate.
comment: NeurIPS 2026 (Position Paper Track)
♻ ☆ NP-LoRA: Null Space Projection for Subject-Style LoRA Fusion NeurIPS 2026
Low-Rank Adaptation (LoRA) fusion enables the composition of subject and style representations for controllable generation without retraining. However, existing approaches primarily operate through weight-level merging, without explicitly modeling how independently trained LoRAs interact in the shared parameter space. We adopt a geometric perspective on LoRA fusion, interpreting content and style LoRAs as occupying overlapping, non-orthogonal low-rank subspaces, where such overlap can lead to conflicting parameter updates that affect generation quality. This observation motivates us to reformulate LoRA fusion not merely as parameter combination, but as a problem of controlling how updates from overlapping subspaces are combined. Based on this insight, we propose Null Space Projection LoRA (NP-LoRA), a training-free framework that employs projection as a fusion operator to explicitly modulate cross-LoRA interactions. Specifically, NP-LoRA uses principal directions of the style LoRA to define a projection subspace and projects the content LoRA onto the complementary subspace (i.e., the null space of the style LoRA), suppressing interference along dominant style directions while preserving complementary information. To avoid the overly aggressive suppression of hard projection, we further formulate soft projection as a regularized optimization problem that balances content preservation against style-subspace suppression. This objective admits a closed-form solution, yielding a projection operator controlled by a single parameter that continuously interpolates between linear merging and hard projection. Extensive experiments across multiple pretrained LoRA pairs show that NP-LoRA achieves more balanced content-style composition compared to strong baselines, without requiring retraining. Code is available at https://github.com/blackzebra7/NP-LoRA.
comment: Accepted at NeurIPS 2026 (poster)
♻ ☆ Hidden Commitment: When Language Models Silently Pick a Side and How Steering Can Surface It ICML 2026
Language-model answers are often treated as a visible record of the internal decision that produced them. We test this assumption in conflicting-context question answering, where two supplied documents support incompatible answers. A model can process the disagreement and still return a one-sided answer, even under prompts that ask it to disclose conflict. In Qwen 0.8B, hidden source commitment is captured by a residual source-choice direction whose sign tracks which document the answer uses. Sparse middle-layer MLP writers feed the same coordinate, and steering the coordinate changes the chosen source. A transfer run on Gemma 3 1B preserves the source-choice readout and sparse-write bridge under LLM-audited labels. Conflict disclosure follows a different pattern, appearing weaker, later-layer, and only partially steerable. These results support a practical monitoring strategy for retrieval systems that detects silent source commitment with residual readouts and pairs conflict-surfacing prompts with disclosure steering when the model has selected a side under disagreement.
comment: Presented at AI4Good @ ICML 2026
♻ ☆ Towards Diverse and Comprehensive Benchmarks for Mutual Information Estimation
Alberto Foresti, Ivan Butakov, Alexander Tolmachev, Giulio Franzese, Alexey Frolov, Pietro Michiardi
Mutual information estimation is a central problem in machine learning and statistics; however, existing benchmarks typically evaluate estimators on simplified, low-dimensional distributions, leaving their performance on complex, realistic data largely unexplored. We address this gap with a comprehensive benchmarking framework grounded in a unified copula-theoretic perspective that subsumes existing benchmarks as special cases. Within this framework, we propose two complementary families of tests: a copula-first family that systematically varies ground-truth mutual information, dimensionality, and marginal complexity using synthetic and flow-based transformations; and a marginals-first family that couples real-world image data with controlled dependency structures, extending the classic same-class-pairing paradigm. We use this suite to extensively evaluate three classes of estimators: non-parametric, variational, and generative. Contrary to prevailing assumptions, our results indicate that there is no universal winner: each category can systematically outperform all other estimators under specific setups. By analyzing these cases, we identify fundamental estimation barriers and propose new tests that more effectively stress these specific limitations. We share the open source code at https://github.com/VanessB/mutinfo.
♻ ☆ How Do Video Foundation Models Encode Intuitive Physics? Probing Across Pretraining Paradigms NeurIPS 2026
Samuele Punzo, Niccolò Caselli, Ippokratis Pantelidis, Francesco Massafra, Salvatore Lo Sardo, Mohammadreza Salehi
Do video foundation models encode a usable sense of how the physical world works, and does intuitive physics emerge naturally from large-scale video pretraining? In this work, we study whether pretrained video foundation models encode intuitive-physics information in their frozen representations, and how its accessibility varies across pretraining paradigms, layers, backbone variants, and probe types. Using frozen-feature probing on IntPhys2 and Minimal Video Pairs (MVP), we compare predictive joint-embedding models (V-JEPA), masked reconstruction models (VideoMAE), and diffusion-based video generators (LTX-Video). We find that across linear, MLP, and temporal attentive probes, physics-relevant information is generally weakest in early layers and most accessible at intermediate to late depth. On MVP, temporal attentive probes substantially outperform linear readouts, with V-JEPA achieving 95.0% pair consistency. On IntPhys2, linear probes recover more signal, and VideoMAE attains the strongest temporal attentive result at 73.9% violation-of-expectation accuracy. On both benchmarks, attentive probe performance on LTX consistently underperforms compared to the other two pretraining paradigms. Together, these results suggest that intuitive-physics is broadly decodable from video foundation models, but it is not represented as a single, uniform capability. Its accessibility, and especially its temporal grounding, depends much more on the benchmark and readout than on a simple hierarchy of pretraining objectives or model scale.
comment: Accepted at the NeurIPS 2026 Workshop on World Models in Physical AI
♻ ☆ VEvo-Harness: Causally-Guarded Self-Evolving Harness for Agentic Image Generation
The central bottleneck in visual generation and editing has migrated from achieving pixel-level fidelity to satisfying densely constrained user specifications through increasingly agentic workflows. Prevailing agentic generation pipelines remain shallow generate-verify-rewrite loops treating VLM judgments as coarse visual feedback, either discarding repair experience or admitting ungated updates that poison skill library. With memorize and govern periods left open, they degrade into ungoverned iterators of repeated trial-and-error rather than continual self-evolving harness. Towards this issue, we present \textbf{VEvo-Harness}, the first causally-guarded continual self-evolving harness system for agentic visual generation. VEvo-Harness introduces four coupled innovations: (1) \textit{a visual traceback} that densifies sparse judgments into root-cause attributions with a hierarchical verification tree; (2) \textit{a visual continual governance} that admits only causally valid skill patches through counterfactual dual-arm image generation replay; (3) \textit{a lifecycle-managed harness state} that hierarchically distills root-cause-anchored episodes across working, experience, and pattern tiers into a persistent skill library of routable and causally verified capabilities; (4) \textit{a heterogeneous sandbox policy} that orchestrates a diverse action space of repair primitives, dynamically routing cost-aware interventions based on root causes to select the most effective strategy. VEvo-Harness surpasses state-of-the-art agentic methods and rivals proprietary generators (\textit{e.g.}, GPT-Image, Nano-Pro) while maintaining the continual harness evolution and generalization.
♻ ☆ Sparkle: Realizing Lively Instruction-Guided Video Background Replacement via Decoupled Guidance NeurIPS 2026
In recent years, open-source efforts like Senorita-2M have propelled video editing toward natural language instruction. However, current publicly available datasets predominantly focus on local editing or style transfer, which largely preserve the original scene structure and are easier to scale. In contrast, Background Replacement, a task central to creative applications such as film production and advertising, requires synthesizing entirely new, temporally consistent scenes while maintaining accurate foreground-background interactions, making large-scale data generation significantly more challenging. Consequently, this complex task remains largely underexplored due to a scarcity of high-quality training data. This gap is evident in poorly performing state-of-the-art models, e.g., Kiwi-Edit, because the primary open-source dataset that contains this task, i.e., OpenVE-3M, frequently produces static, unnatural backgrounds. In this paper, we trace this quality degradation to a lack of precise background guidance during data synthesis. Accordingly, we design a scalable pipeline that generates foreground and background guidance in a decoupled manner with strict quality filtering. Building on this pipeline, we introduce Sparkle, a dataset of ~140K video pairs spanning five common background-change themes, alongside Sparkle-Bench, the largest evaluation benchmark tailored for background replacement to date. Experiments demonstrate that our dataset and the model trained on it achieve substantially better performance than all existing baselines on both OpenVE-Bench and Sparkle-Bench. Our proposed dataset, benchmark, model, as well as the full data generation pipeline, are fully open-sourced at https://showlab.github.io/Sparkle/.
comment: NeurIPS 2026 E&D Camera Ready Version; Project Page: https://showlab.github.io/Sparkle/
♻ ☆ BiHashFormer: Hash-Driven Dual-Branch Transformer for Efficient Object Detection in HRW Shots NeurIPS 2026
Recent advances in gigapixel-level imaging have brought High-Resolution Wide shots to the forefront of research. However, these images present significant challenges: extreme sparsity of foreground, gigapixel-level resolutions and interleaving of foreground and background. This causes traditional detectors and attention mechanisms to be hindered by background, resulting in inefficiency and inaccuracy. To tackle this problem, we propose BiHashFormer, a dual sparsified branch transformer built on hashing. RoI Selector will predict the foreground proportion for top-k selecting target-containing windows. The compression branch processes windows selected and refines attention into HashAttention by discarding half of the key-value pairs, reducing cost and improving efficiency. The compensation branch uses HashMiner to perform low-cost hash searches in the remaining regions, recovering features of missed objects. Experiments confirm that the selection relationship in HashAttention is one-way and reveal the preference of different queries for keys. In experiments on the gigapixel benchmark PANDA, BiHashFormer reduces 37.5% backbone FLOPs while improving AP50 to 81.0%. Moreover, this parallel dual-branch structure allows the model to avoid inference delays caused by inter-branch dependencies, while achieving much greater stability than single-branch models, specially with low rates of RoI selection.
comment: Accepted at NeurIPS 2026. 34 pages including supplementary material and the NeurIPS checklist. Code: https://github.com/Purecoo00/BiHashFormer
♻ ☆ PACE: Pareto-Adaptive Compression for Efficient Native MLLMs NeurIPS 2026
Native multimodal large language models (native MLLMs) jointly encode visual and linguistic sequences within a unified autoregressive Transformer, emerging as a promising paradigm for multimodal understanding. Unlike conventional ViT-based MLLMs relying on external vision encoders, ViT-free native MLLMs tokenize pixel patches directly, potentially suffering severe visual token redundancy. A pilot study confirms this: unlike ViT-based models that degrade under compression, ViT-free MLLMs tolerate aggressive compression with near-lossless or even improved performance. Yet existing compression methods target ViT-based settings, leaving native MLLMs, which stand to gain the most, unexplored. To close this gap, we introduce an analysis pipeline centered on token discriminability and cross-dimensional heterogeneous redundancy, revealing that (i) redundancy is distributed independently and heterogeneously along height and width axes, and (ii) early-layer visual tokens are indiscriminable and uniformly redundant. Guided by these findings, we propose PACE (Pareto-Adaptive Compression for Efficient Native MLLMs), the first visual token compression framework tailored for native MLLMs with Multimodal-RoPE. Leveraging dimensional decoupling of positional encoding, PACE decomposes attention into two orthogonal spatial saliency estimates along height and width, gated by a positional attention signal, yielding direction-aware importance with negligible overhead. It further casts token budget allocation as Pareto optimization over performance preservation, inference efficiency, and spatial coverage, jointly identifying optimal compression depths and retention ratios. Experiments show PACE maintains near-lossless performance at only 25%-50% visual token retention, even improving several benchmarks, while delivering over 2x FLOPs reduction.
comment: NeurIPS 2026 Accepted Paper
♻ ☆ DRScaffold: Boosting Dense-Scene Reasoning in Lightweight Vision Language Models
Lightweight vision-language models perform competitively on standard benchmarks yet fail systematically in dense-scene reasoning, where multiple objects, attributes, and relations must be jointly grounded and resolved through multi-step inference. Such capability is critical for real-world applications where models must reliably interpret cluttered environments. Yet existing training signals provide no explicit grounding between reasoning steps and the underlying visual entities and relations, leaving lightweight models free to generate fluent but visually unanchored reasoning chains. To address this gap, we first introduce DRBench, a benchmark of 14,573 questions across 2,943 images, organized into five task categories spanning three progressive reasoning layers. Building on DRBench, we propose DRScaffold, a supervised fine-tuning framework that decomposes the supervision target into four causally ordered stages, promoting grounded reasoning without architectural modification. Experiments on three lightweight VLMs demonstrate substantial gains on DRBench while preserving or improving performance on general-purpose benchmarks. Notably, Qwen2.5-VL-3B trained with DRScaffold surpasses the frozen Qwen2.5-VL-32B under zero-shot direct-answer prompting on DRBench, demonstrating that structured supervision can substitute for a significant portion of model scale in dense-scene reasoning. Our code is available at https://github.com/irene-shi/DRScaffold.
comment: 32 pages
♻ ☆ Place-it-R1: Unlocking Environment-aware Reasoning Potential of MLLM for Video Object Insertion
Video object insertion is fundamental to video editing, yet existing diffusion methods often produce visually plausible but physically inconsistent results. We present Place-it-R1, an end-to-end framework for physically plausible video object insertion driven by environment-aware MLLM reasoning. Rather than treating reasoning as a generic text prompt, Place-it-R1 uses the MLLM to analyze the target environment, infer object-scene interactions, and determine where an insertion is physically valid. The resulting reasoning is translated into two complementary forms of guidance for video diffusion: semantic guidance that describes the intended physical interaction and spatial guidance that provides a valid insertion region in each frame. To further align generation with local physical realism, we introduce Spatial Direct Preference Optimization, which leverages an MLLM to rank generated candidates, and introduces a region-aware preference objective that explicitly localizes physical-violation penalties to the inserted-object region. Place-it-R1 further offers flexible and standard modes to trade off environment adaptation and scene preservation. Extensive experiments show that Place-it-R1 produces more physically coherent and visually natural insertions than state-of-the-art methods and achieves competitive results against commercial systems.
comment: https://nevsnev.github.io/Place-it-R1/
♻ ☆ Why VLMs Miss Small Objects, and When Zooming In Is Safe
Screenshots, documents, engineering drawings, shelves and maps hold many small objects of kinds that change from task to task, and vision-language models (VLMs) promise to find them from an instruction and an example. Yet they see each image through a fixed token budget and miss small objects, so every user, and every agent that calls a model, must choose whether to send an image whole, raise its resolution, split it, or zoom. We show that VLMs miss small objects for two reasons a caller can estimate before any call: an object gets too few visual tokens (S), and a call must search and report too much (L). Raising S by a factor a multiplies the tokens needed to see an image by a^2, while splitting at the same scale lowers L at a cost at most linear in the number of views. Under two monotonicity assumptions, splitting cannot lower expected recall if no view zooms out and views overlap by one object, and neither part can be dropped; every such split pays a minimum token cost, and a simple rule pays it up to an explicit factor. Better models need less S, but an image can be arbitrarily large while a call's budget is fixed, so splitting does not become obsolete, for zoom agents either. In about 187,000 requests to 12 VLMs, up to GPT-6.1, Claude Opus 5.5 and Gemini 4, on controlled images 24 of 40 predicted orderings hold significantly and none is violated; splits that never zoom out raised recall on average over models wherever S was below 1.5, and splits that zoom out lowered it in 19 of 21 model and data settings. The rule raised recall significantly in five of ten settings, by up to 0.28 (from 0.62 to 0.90), and precision and F1 in six, at a token cost known before any call.
♻ ☆ MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation IEEE
Federated Learning enables decentralized training by aggregating model updates across clients without sharing raw data, while Split Federated Learning further partitions the model between clients and a server to reduce computation and communication at the client side. However, decentralized medical institutions rarely operate on a single shared task, making standard Federated and SplitFed collaborations poorly aligned with real clinical workflows. Multi-task FL extends these frameworks by allowing clients to handle different tasks, but often introduces instability and privacy vulnerabilities. This study proposes \textbf{MuCALD-SplitFed}, a multi-task SplitFed framework that integrates causal representation learning and latent diffusion. Experiments show MuCALD-SplitFed consistently improves segmentation, while baseline SplitFed fails to converge. The proposed approach further reduces information leakage at split points, mitigating reconstruction-based and membership inference attacks. Additionally, MuCALD SplitFed outperforms state-of-the-art personalized FL and multi-task FL approaches. The code repository is: https://github.com/ChamaniS/MuCALD_SplitFed.
comment: Accepted to oral presentation and conference proceedings at IEEE International Conference on Image Processing (ICIP 2026), Finland
♻ ☆ GUI-Lens: Enabling Precise GUI Grounding for Off-the-Shelf VLMs via Coarse-to-Fine Cropping
Zichuan Fu, Shirong Wang, Wenlin Zhang, Guojing Li, Yimin Deng, Jingtong Gao, Junjia Qi, Hanyu Yan, Yefeng Zheng, Xiaopeng Li, Wanyu Wang, Xian Wu, Xiangyu Zhao
GUI grounding maps natural-language instructions to click locations and is essential for reliable GUI agents. The task remains difficult on high-resolution, densely populated interfaces because a vision-language model (VLM) may recognize a requested control without locating it precisely enough for interaction. Most existing methods provide various forms of localization assistance, but still rely on a direct click prediction, allowing visual ambiguity or an inaccurate initial estimate to propagate to the final result. In this paper, we introduce GUI-Lens, a coarse-to-fine grounding framework that allows a general-purpose VLM to determine the target through active visual observations. Specifically, GUI-Lens extracts OCR text and detected UI components from the screenshot and presents their positions as coordinate references. Using the instruction, the current view, and these references, the VLM selects the region and scale of the next view, which is cropped and enlarged to provide finer visual details. This process continues over successively focused views until the target is determined. Proposed crops and clicks are checked against the instruction throughout the process, and the final local position is mapped back to the original screen coordinates. Experiments on five GUI grounding benchmarks and six general-purpose base models show that GUI-Lens consistently improves grounding accuracy, achieving up to 9.3 times the accuracy of direct grounding on ScreenSpot-Pro.
comment: Preprint. Code: https://github.com/wangshirong123/GUI-Lens ; Project page: https://wangshirong123.github.io/GUI-Lens/
♻ ☆ Autoregressive Diffusion for Compositional Text-to-Scene Generation
Zhenggang Tang, Yuehao Wang, Yuchen Fan, Jun-Kun Chen, Yu-Ying Yeh, Kihyuk Sohn, Zhangyang Wang, Qixing Huang, Alexander Schwing, Rakesh Ranjan, Dilin Wang, Zhicheng Yan
Recent text-to-scene generation approaches largely reduced the manual efforts required to create 3D scenes. However, their focus is either to generate a scene layout or to generate objects, and few generate both. The generated scene layout is often simple even with LLM's help. Moreover, the generated scene is often inconsistent with the text input that contains non-trivial descriptions of the shape, appearance, and spatial arrangement of the objects. We present a new paradigm of sequential text-to-scene generation and propose a novel generative model for interactive scene creation. At the core is a 3D Autoregressive Diffusion model 3D-ARD+, which unifies the autoregressive generation over a multimodal token sequence and diffusion generation of next-object 3D latents. To generate the next object, the model uses one autoregressive step to generate the coarse-grained 3D latents in the scene space, conditioned on both the current seen text instructions and already synthesized 3D scene. It then uses a second step to generate the 3D latents in the smaller object space, which can be decoded into fine-grained object geometry and appearance. We curate a large dataset of 230K indoor scenes with paired text instructions for training. We evaluate 7B 3D-ARD+, on challenging scenes, and showcase the model can generate and place objects following non-trivial spatial layout and semantics prescribed by the text instructions.
♻ ☆ IR275K: A Benchmark for Infrared Multi-Frame Super-Resolution Toward Efficient Remote Sensing
Jie Deng, Heyang Wang, Changxin Wang, Junkai Shen, Hongyi Chen, Zhiping He, Hongxing Qi, Xudong Zhang, Jianyu Wang
Infrared multi-frame super-resolution (MFSR) exploits complementary observations to recover spatial detail, but its evaluation remains fragmented across datasets and protocols. A common benchmark is needed to evaluate reconstruction under weak texture, varying thermal contrast, and noise in the source imagery. It should also characterize reconstruction performance across different inter-frame displacement ranges. We introduce IR275K, a curated benchmark containing 594 in- frared video sequences and 275,196 frames. It provides sequence- level train/validation/test splits and a reproducible x4 evaluation protocol. Evaluation spans single-frame and multi-frame recon- struction using seven learned baselines, the CGMamba reference model, and bicubic interpolation. All learned methods are evalu- ated under both bicubic degradation and synthetic mixed sensor noise.EDVRleadstheoverallrankingunderbicubicdegradation, whereas MSDTGP leads under mixed sensor noise. DBSR leads thehigh-displacementUAVsubsetunderbothsettings.Controlled ablations using CGMamba show that real neighboring frames improve PSNR over center-frame repetition by 1.04 dB under bicubic degradation and 0.95 dB under mixed sensor noise. Center-frame conditioning also improves reconstruction under both settings. The benchmark thus reveals how reconstruction rankings and the benefits of neighboring frames depend on evaluation conditions. The complete IR275K dataset is publicly available at: https://github.com/InfraRecon7/IR275K.
♻ ☆ One Step Is Optimal: Unconditional Rectified Flows as Noise2Noise Denoisers for Low-Dose CT
We study rectified flows for denoising without clean training targets. For an unconditional rectified flow trained on Noise2Noise pairs, the optimal velocity field yields the minimum mean squared error (MMSE) denoiser in one step. In a jointly Gaussian model, we show that integrating the flow before applying its denoising readout increases error with integration time. The flow transports between noisy distributions, so its endpoint is not a clean-image estimate. Experiments on low-dose CT and Gaussian-corrupted natural images follow this pattern: one-step flow performs similarly to direct regression on the same pairs, whereas additional Euler steps reduce PSNR. A direct regressor using spatially separated, similar patches gives our best label-free CT result, a $1.99$\,dB gain over the noisy input. We report these results in CTDenoiser, a benchmark covering five architectures, supervised and self-supervised training, and two per-image methods. Supervised CNNs gain about $4$\,dB, while label-free performance depends strongly on how training pairs account for correlated CT noise. A channelized Hotelling observer also shows that fidelity gains need not improve lesion detectability: detectability falls at $160$\,HU despite higher PSNR and SSIM, while changes at $40$--$80$\,HU are small. Code, model definitions, and data-loading utilities will be released publicly; the repository link is withheld for anonymous review.
comment: 15 pages, 3 figures
♻ ☆ MIC-BEV: Multi-Infrastructure Camera Bird's-Eye-View Transformer with Relation-Aware Fusion for 3D Object Detection
Yun Zhang, Zhaoliang Zheng, Johnson Liu, Zhiyu Huang, Zewei Zhou, Zonglin Meng, Tianhui Cai, Jiaqi Ma
Infrastructure-based perception plays a crucial role in intelligent transportation systems by providing global situational awareness and enabling cooperative autonomy. However, camera-based 3D object detection remains challenging under multi-view infrastructure setups, heterogeneous camera configurations, degraded sensing conditions, diverse road layouts, and adverse environmental conditions. We introduce MIC-BEV, a Transformer-based bird's-eye-view (BEV) perception framework for infrastructure-based multi-camera 3D object detection. MIC-BEV supports a variable number of cameras with heterogeneous intrinsic and extrinsic parameters and uses a graph-enhanced fusion module to adaptively integrate multi-view image features by modeling geometric relationships between cameras and the BEV grid. To support training and evaluation, we introduce M2I, a synthetic dataset featuring diverse camera layouts, road geometries, environmental conditions, and sensor-degradation cases. Experiments on the simulated M2I dataset and the real-world RoScenes dataset show that MIC-BEV achieves strong 3D detection performance and improved robustness under challenging conditions. Ablation studies validate the effectiveness of its key design choices, while weight analyses show that the learned fusion weights are associated with view-reliability factors and adapt to degraded camera inputs. We further report computational cost and deployment-related factors, providing additional evidence of practical feasibility and real-world deployment potential. These results indicate that MIC-BEV is a promising framework for infrastructure-based perception. The dataset and source code are available at: https://github.com/HandsomeYun/MIC-BEV.
♻ ☆ On the Faithfulness of Visual Thinking: Measurement and Enhancement NeurIPS 2026
Recent large vision-language models (LVLMs) can generate vision-text multimodal chain-of-thought (MCoT) traces after reinforcement fine-tuning (RFT). However, we observe that the visual information in MCoT is often inaccurate even when answers are correct, and task accuracy remains nearly unchanged despite the visual information being corrupted. This indicates a lack of faithfulness in the vision part of MCoT reasoning. We attribute this to the RL reward design in RFT, which solely incentivizes the format of interleaved vision-text cues, encouraging the model to incorporate visual information into its text reasoning steps without considering its correctness. In this paper, we first probe MCoT faithfulness by measuring how much the prediction changes when its visual and textual thoughts are intervened. Surprisingly, the model's predictions remain nearly unchanged under visual intervention but change significantly under textual intervention, indicating that the visual evidence is largely ignored. To further analyze the visual information, we introduce an automated LVLM-based evaluation metric that quantifies the faithfulness of visual cues from two perspectives: relevance and sufficiency. Our evaluation reveals that the visual information in current MCoT traces is simultaneously irrelevant and insufficient. To address this issue, we propose Sufficient-Component Cause Model (SCCM) learning. This approach encourages the MCoT to generate sufficient yet minimal visual components that are independently capable of leading to the correct answer. The proposed SCCM is annotation-free and plug-and-play, compatible with various RFT for MCoT. Empirical results demonstrate that SCCM substantially improves the visual faithfulness across a suite of fine-grained perception and reasoning benchmarks. Code is available at https://github.com/EugeneLiu01/Faithful_Thinking_with_Image.
comment: NeurIPS 2026 Poster
♻ ☆ AdaGS: Confidence-Gated Diffusion Adaptation for Unposed Sparse-View 3D Gaussian Reconstruction
Sparse-view outdoor 3D reconstruction remains challenging for Gaussian splatting. Although diffusion restorers can provide additional pseudo-view supervision, we identify a failure pattern in diffusion-guided reconstruction: an unguided diffusion model may misinterpret floating artifacts and complete them into plausible but non-existent scene structures. To mitigate this challenge, we propose a confidence-gated diffusion adaptation framework for unposed sparse-view reconstruction. We introduce an artifact-aware adapter that is trained in two stages on our constructed Gaussian artifact dataset. The adapter suppresses floater-induced visual cues before diffusion restoration, while bidirectional confidence fusion accepts generated regions only when they are supported by cross-view geometric evidence. Furthermore, we introduce a Gaussian stabilization strategy to attenuate Gaussians that exhibit weak multi-view inconsistency, preventing them from repeatedly triggering hallucinations in later optimization. Extensive experiments on three outdoor datasets demonstrate that our method improves rendering fidelity and pose accuracy over existing approaches.
comment: 9 pages
♻ ☆ Unlimited OCR Works 2
Shu Wei, Jingjing Wu, Qunyi Xie, Lingshu Zhang, Xiangcheng Du, Yannian Fu, Mengjie Wang, Zuyi Wang, Zhichao Liu, Yangliu Xu, Youyang Yin, Huanhuan Liu, YY, Yi Yang
Unlimited OCR ushers the era of long-horizon parsing, substantially mitigating the growth of working memory as document lengthens. However, as document length scales to hundreds of pages, the visual context grows substantially, sharply increasing the computational cost of each decoding step and limiting the scalability of ultra-long document parsing. Inspired by human book transcription via localized focus without re-scanning preceding pages, we introduce Unlimited OCR 2, a document parsing model that looks only at what it transcribes. Building on Unlimited OCR, we upgrade all the decoder attention modules to Sparse Reference Sliding Window Attention (SR-SWA), bounding active reference tokens to a fixed capacity and thus sustaining constant computational costs across arbitrarily long documents. By combining SR-SWA with Unlimited-Horizon Decoding Paradigm, Unlimited OCR 2 enables document parsing over virtually unlimited lengths without prohibitive memory overhead. At the hundred-page scale, Unlimited OCR 2 achieves 7.0$\times$ and 5.6$\times$ per-step speedups over Full Attention and R-SWA, respectively. Furthermore, it delivers state-of-the-art end-to-end performance on both MPDocBench and MarathonDocBench, our newly proposed benchmark targeting hundred-page document parsing. Ultimately, the capacity for uninterrupted, one-shot document parsing signifies a fundamental paradigm shift into the OCR 3.0 era.
♻ ☆ Knowledge-structured and evidence-aware semantic self-distillation for glomerular pathology foundation model
Qiming He, Jing Li, Zihan Wang, Tian Guan, Yifei Ma, Zimo Zhao, Yanxia Wang, Hongjing Chen, Yexing Zhang, Yizhi Wang, Xinrui Chen, Xitong Ling, Minxi Ouyang, Yujie Xie, Lianghui Zhu, Yiqing Liu, Qingxia Hou, Shuyan Zhao, Xiaoqin Wang, Lili Ma, Qiang Huang, Zhiyuan Shen, Junru Cheng, Siqi Zeng, Chao He, Yonghong He, Zhe Wang
Visual foundation models learn rich pathological morphology but lack explicit concept-level supervision. Vision--language pretraining can provide this supervision but requires costly image--text pairs. Here, we introduce GloSAGE, a semantic self-distillation framework that uses lesion profiles predicted by a pretrained visual teacher to construct structured pathology descriptions. Reliability-guided selection identifies image--text anchors, while Knowledge-Structured Pathology Text Synthesis and Evidence-Guided Negative-pair Construction account for partially observed lesions. Image--text alignment trains projection layers while retaining the frozen visual encoder; an intermediate Semantic Transfer Layer supplies features for downstream tasks. Developed on GloMillion and evaluated across seven glomerulus- and case-level cohorts, GloSAGE supported zero-shot lesion recognition, image--text retrieval, hard-negative discrimination and concept-intervention analyses. It outperformed its source visual model in most lesion-recognition tasks and improved lesion grading, while supporting competitive case-level disease classification, injury grading and baseline renal function estimation. Ablations supported the combined contributions of knowledge-structured text and evidence-guided negatives, and the intermediate transfer layer improved mean lesion-recognition ROC-AUC relative to the complete projection output. A frozen language model conditioned on GloSAGE features generated glomerular descriptions. These findings suggest that teacher-derived lesion profiles can provide annotation-efficient semantic supervision for extending a glomerular visual foundation model.
♻ ☆ DRT: Dense Reasoning Trace for Efficient and Grounded Multimodal Reasoning
Despite the remarkable progress in Multimodal Large Language Models (MLLMs), prevailing Chain-of-Thought (CoT) paradigms remain confined to the natural-language expression space. Consequently, they inherently incur excessive linguistic overhead, leading to information dilution and weak visual grounding. To address this challenge, we propose Dense Reasoning Trace (DRT), a paradigm that departs from natural-language-centered CoT by expressing reasoning as compact structured traces, which include concise intermediate states with symbolic connectors and disentangle visual observations from logical deductions. First, we introduce the Dense Trace Initialization to internalize the DRT reasoning mode into the model, substantially improving token efficiency while preserving visual evidence. To further enable the model to faithfully capture the logical relations within traces, we propose the Trace-Grounded Reinforcement Learning framework, which builds reference traces through a tri-perspective verification pipeline and employs Trace-Grounded GRPO with structured rewards, encouraging the model to generate concise DRT-style traces with reduced hallucination and stronger logical grounding. Extensive experiments on challenging reasoning benchmarks show that DRT achieves 5.5$\times$ token efficiency improvement while improving 1.3 accuracy points over the Qwen3-VL baseline. These findings suggest that complex multimodal reasoning may not require verbose natural-language traces, opening a more efficient path for next-generation MLLMs. Our code and data are available at: https://github.com/HIT-leaderone/DRT
♻ ☆ Learning Goal-Conditioned Subgoals through Frozen Hierarchical World Models
Hierarchical world models plan over long horizons by passing predicted states from a coarse model to a lower-level controller. Choosing these states can be expensive: the upper-level planner repeatedly differentiates through candidate macro-action trajectories. We replace this search with a lightweight goal-conditioned network trained through the frozen high-level predictor. The network supplies a conditioning vector, while the predictor generates the next latent waypoint. Training uses hindsight goals and state-prediction losses on offline trajectories, without search-solution targets or macro-action labels. Inference retains an autoregressive high-level rollout, but requires no high-level candidate optimization. We evaluate this approach on the shared-latent HWM checkpoints distributed with H-JEPA, using three backbone seeds and three head initializations per backbone. With a common evaluation protocol, mean success is 70.4% on Cube, 48.7% on Push-T, 100.0% on FourRoom, and 71.1% on Visual AntMaze. The method improves over the corresponding HWM search baseline on three environments and has the same mean on Push-T. Dedicated measurements on an NVIDIA L40S give 1.28-4.39x end-to-end speedups for batched evaluation. Ablations identify state-adaptive conditioning as a consistent contributor, while predictor-mediated subgoals and unrestricted conditions have environment-dependent benefits.
♻ ☆ Can We Change "Stroke Size" to Simplify Flow Matching?
Deep-learning-based image generation models are useful but difficult to train. Across the different time steps along a generative path, high-noise states present distinct optimization and prediction challenges. Inspired by the varying stroke sizes used in human painting, we hypothesize that applying rough strokes early and fine strokes late can simplify flow-matching image generation. We develop MultiStroke, which progressively varies the spatial granularity of the clean-signal component in the velocity regression target. A lightweight corrector aligns adjacent roughness levels during sampling. On ImageNet-1K, MultiStroke reduces FID by $22.5\%$ relative to a matched flow-matching baseline, despite the limited corrector capacity. Code is available at https://github.com/DaChengRuoQue/MultiStroke-Code. A quick video explanation is available at https://dachengruoque.github.io/MultiStroke-Web/.
♻ ☆ Gaussian Splatting-based Volumetric Video Compression with Sparse 4D Anchors
Ge Gao, Siyue Teng, Chanqgi Wang, Fan Zhang, Nantheera Anantrasirichai, Jui Chiu Chiang, Wen-Hsiao Peng, David Bull
Immersive video communication requires photorealistic, compact dynamic scene representations that support efficient rendering. 3D Gaussian Splatting (3DGS) is a promising approach, but dynamic 3DGS remains difficult to compress due to its dense primitives and spatiotemporal redundancy. Anchor-based formulations improve compactness through sparse scaffolds that share geometry and appearance across primitives. However, existing designs typically deform a single canonical scaffold and condition each primitive solely on its associated anchor, limiting their ability to model non-local dynamics and disocclusions and exploit inter-anchor correlations. To address these limitations, we propose SAGA, a volumetric video codec built on Sparse Anchor-assisted GAussian splatting representations. SAGA represents a dynamic scene using hierarchical sparse anchors in 4D space-time. Coordinate-based INR decoders generate fine anchors and Gaussian primitives from features obtained through kernel interpolation among nearby anchors, enabling parameter sharing across spatiotemporal structures. A fixed-size memory with a novel orthogonality-informed slot-update strategy further captures long-range dependencies among unstructured anchors for entropy coding. Empirically, \name achieves PSNR-based BD-rate reductions of 90.88%, 64.30% and 65.14% relative to GIFStream, TED-4DGS, and OMG4, respectively, on the Neu3D testset.