Computer Vision
GS-RealBlur: A Flexible Data Acquisition Framework for Real-World Image Deblurring
Mingyang Chen, Zhilu Zhang, Honglei Xu et al. (+3 more)
High-quality, large-scale paired data is essential for training learning-based image deblurring models. However, synthetic blurry images generally lack realism, while real-world captured images require complex and inflexible camera systems. In this work, we propose GS-RealBlur, a data acquisition framework for real-world image deblurring, achieving both blur realism and acquisition flexibility. Specifically, we use a handheld camera to capture blurry images, and deploy a gimbal to densely capture sharp images of the same scene. We reconstruct the 3D representation of sharp images and calibrate the camera pose of each blurry frame within this 3D. The image rendered from this 3D according to the pose serves as the sharp counterpart. To better align the rendered image with the blurry image, we introduce a Blur-aware Pose Refinement (BPR) module that refines the pose using appearance consistency and centroid
alignment constraints. Leveraging GS-RealBlur, we construct a high-quality and diverse
dataset. Extensive experiments demonstrate that a deblurring model trained on our
dataset achieves superior generalization performance across various real-world deblurring
benchmarks, consistently outperforming models trained on existing synthetic and real-world
datasets. The code and
dataset will be made publicly available.
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All authors Mingyang Chen, Zhilu Zhang, Honglei Xu, Renlong Wu, Xiaohe Wu, Wangmeng Zuo
Computer Vision
Unsupervised Keypoints for Real-Time Fall Detection: Comparative Analysis Under Real-world Conditions with Predictive Bandwidth Reduction
Tasmiah Haque, Jacob Kosinski, Sumit Mohan et al. (+2 more)
Falls among older adults are a major safety and health-systems challenge, yet continuous in-person monitoring is difficult to sustain across home and clinical care settings.
Video-based monitoring can capture fall-relevant motion, but scalable real-time deployment is limited by
privacy, compute, and bandwidth constraints, and existing keypoint-based methods typically rely on supervised or anatomical pose representation, which is vulnerable to occlusion and partial body visibility. We propose a fall-monitoring framework that replaces continuous
video transmission with compact motion representations, using unsupervised keypoints which are extracted locally, and a variational recurrent model is used to forecast motion at the staff end, followed by fall classification. We evaluate the framework on the UR Fall and Human Fall
datasets under random, subject-disjoint, and occlusion-based splits to systematically characterize when each representation has an advantage. We find that random splits do not discriminate between representations, and under subject-disjoint evaluation no uniform advantage emerges; performance varies across held-out subjects with their visual characteristics. Under occlusion, however, unsupervised keypoints substantially outperform supervised keypoints, retaining strong detection sensitivity where supervised keypoints miss approximately half of falls; this advantage reflects their anatomical independence and persists under bandwidth-constrained prediction. The unsupervised detector also requires roughly two orders of magnitude less computation, supporting
privacy-preserving, bandwidth-aware, always-on fall monitoring.
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All authors Tasmiah Haque, Jacob Kosinski, Sumit Mohan, Mohammad Abdullah Al-Mamun, Srinjoy Das
Computer Vision
Partial Information Decomposition as a Multi-Contrast 3D MRI Selection Strategy for Resource-Constrained Deep Neural Network Training in Brain Tumor Segmentation
Agamdeep Chopra and Mehmet Kurt
Multi-contrast 3D MRI
segmentation can be computationally demanding when all available sequences are used. We evaluate a pre-training Partial Information Decomposition framework that ranks input pairs according to their redundant, unique, and synergistic information about regional tumor burden and selects the highest-ranked pair for downstream training. Applied to T1n, T1c, T2w, and T2-FLAIR MRI, the framework selected T1c+T2-FLAIR. We then trained eleven architecturally identical lightweight 3D U-Nets using different input configurations. On an independent test cohort, T1c+T2-FLAIR was the strongest two-input configuration and ranked second overall in mean Dice (0.676 versus 0.687 for all four inputs). Independent Shapley analysis on the full-input model also identified T2-FLAIR and T1c as the most influential inputs and their pairwise interaction as the strongest. These findings demonstrate the practical value of PID based pre-training selection for identifying compact, informative MRI input sets before costly 3D model development.
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Computer Vision
Reasoning-Guided Part-Level Visual Grounding via Reinforcement Learning
Kazi Sajeed Mehrab, Hani Alomari, Najibul Haque Sarker et al. (+4 more)
Multimodal large
language models (MLLMs) ground whole objects well from free-form
language queries, but they struggle when the query names a part rather than the object. We trace this to a missing object-part hierarchy, since parts are localized in the same single step used for objects. We propose Object-Part Hierarchical Reflective Grounding (OP-HRG), a coarse-to-fine
reasoning-guided grounding strategy that first localizes the parent object and then the part within it. A self-check then reflects on the result, with an extension to re-encode the predicted crop to inspect the region it is correcting. We introduce a part-aware GRPO framework to train our pipeline with stage-wise rewards. A 4B model trained this way outperforms 7B grounding LLMs and SAM3 across PascalPart, PartImageNet, and InstructPart, and transfers to
reasoning segmentation.
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All authors Kazi Sajeed Mehrab, Hani Alomari, Najibul Haque Sarker, Chia-Wei Tang, Zaber Ibn Abdul Hakim, Anuj Karpatne, Chris Thomas
Computer Vision
Training-Free Open-Vocabulary 3D Point-Cloud Segmentation on the Generalized Few-Shot Benchmark
Silas kwabla Gah and Ebenezer Owusu
Generalized few-shot 3D point-cloud
segmentation (GFS-PCS) asks a model to segment a scene into many base classes seen at training time and a set of novel classes. The state of the art reaches novel classes by reconciling a dense but noisy 3D
vision-
language prior with the few-shot support, but it pays for this with base 3D labels, per-episode training, and the support annotations themselves. We ask how far the same reconciliation can go with none of these: no training, no 3D labels, and not even the few-shot support. We pair a frozen 3D
vision-
language model (RegionPLC) as a dense prior with a frozen promptable concept segmenter (SAM3), prompted by the bare novel class names and lifted from posed RGB views, and reconcile the two by cross-view consistency: a point becomes novel only when enough of the views that see it agree. On the ScanNet200 GFS-PCS
benchmark this fully training-free, open-vocabulary pipeline improves novel mIoU by +2.6 over the training-free dense prior while holding base accuracy within 0.5, and recovers a third (33%) of the novel-class gap to the trained state of the art that uses far more supervision. We further show that injecting the few-shot support into the pipeline, as a fusion gate and as a prototypical dense classifier, adds nothing over consistency alone and in fact degrades it through the classifier, which is why the method needs no support at all. On the harder ScanNet++
benchmark, where the dense prior is far weaker on novel classes, the same pipeline nearly doubles novel mIoU (+15.7, from 16.2 to 31.9) at a 1.7 base cost, lifting the harmonic mean from 21.5 to 31.1
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Computer Vision · Cross-listed from cs.RO
Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories
Xiaomi Robotics Team: Jun Guo, Piaopiao Jin, Jason Li et al. (+30 more)
We present Xiaomi-Robotics-1, a foundational
vision-
language-action (VLA) model capable of (1) following diverse
language instructions to perform a wide range of mobile manipulation tasks in unseen environments out-of-the-box, and (2) efficiently adapting to novel downstream tasks with minimal fine-tuning data. We propose a two-stage training recipe consisting of pre-training and post-training. During pre-training, we imbue the model with broad and generalizable action-generation capabilities by training on over 100k hours of real-world manipulation trajectories collected via UMI devices. Crucially, we develop a scalable auto-labeling pipeline that annotates trajectory clips with natural
languages describing scene state transitions, providing rich and precise conditioning for action learning. During post-training, we aim to align these capabilities with
robot embodiments and imperative instructions that humans naturally use to
prompt robots. Extensive experiments demonstrate strong scaling behavior. Xiaomi-Robotics-1 consistently improves with increased data scales and model sizes during pre-training. This scaling behavior directly transfers to post-training, where a stronger pre-training model yields better out-of-the-box real-
robot performance in unseen environments. Furthermore, Xiaomi-Robotics-1 serves as a strong
robot foundation policy that can be efficiently fine-tuned on complex, dexterous tasks with high data efficiency. Across multiple simulation
benchmarks, Xiaomi-Robotics-1 outperforms state-of-the-art methods. Notably, it establishes a new state-of-the-art with a 57.4% success rate on RoboCasa365, surpassing the previous best of 46.6%. Furthermore, it achieves an average score of 20.07 on RoboDojo, significantly outperforming the prior state-of-the-art (13.07). Code and model checkpoints will be released. Project page: https://robotics.xiaomi.com/xiaomi-robotics-1.html
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All authors Xiaomi Robotics Team: Jun Guo, Piaopiao Jin, Jason Li, Peiyan Li, Yingyan Li, Futeng Liu, Wanli Peng, Optimus Qin, Yifei Su, Nan Sun, Qiao Sun, Runze Suo, Heyun Wang, Yunhong Wang, Rujie Wu, Caoyu Xia, Lina Zhang, Jack Zhao, Guoliang Chen, Wenlong Chen, Xinze He, Bin Li, Qing Li, Zhuorong Li, Heng Qu, Wenxuan Song, Diyun Xiang, Yifan Xie, Peiran Xu, Hangjun Ye, Wen Ye, Han Zhao, Quanyun Zhou
Computer Vision
Rethinking the Readout: Unlocking Video Backbones for AI-Generated Video Detection
Manni Cui, Ziheng Qin, ZiAn Wang et al. (+8 more)
AI-generated
videos (AIGVs) typically contain subtle temporal artifacts that arise from inter-frame inconsistencies rather than within individual frames. A detector that captures such artifacts should therefore benefit from
video pretrained backbones over image only ones. In practice, however,
video backbones with standard global readouts often fail to outperform strong image pretrained probes on AIGV
benchmarks. We attribute this gap to excessive spatiotemporal aggregation in the readout.
Video pretrained backbones tend to compress each frame into a single global descriptor. This compression suppresses local patch level temporal dynamics and discards inter patch relations, which are precisely the cues that AIGV detection most reliably depends on. Based on this, we propose Velocity Gated Patch Velocity Profiling (V-PVP), a lightweight readout that replaces only the aggregation layer with two parallel streams over the patch velocity field, adding only about $0.5$M trainable parameters. V-PVP serves as a general plug-and-play module that consistently improves performance across diverse
video backbones under both end-to-end fine-tuning and linear probing settings. Our method reaches 95.28 AUC on AIGVDBench while keeping the backbone fully frozen. The results show that simply replacing the aggregation layer reactivates the temporal potential of frozen
video backbones, restoring their advantage on AIGV detection. Code is available at https://anonymous.4open.science/r/PVP-81B3/.
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All authors Manni Cui, Ziheng Qin, ZiAn Wang, Ruiqi Liu, Dianyuan Zou, Jianglan Wei, Han Zhou, Yu Liu, Jingrui Xu, Wenhao Wang, Zhenyu Zhang
Computer Vision
Hierarchical Denoising For Multi-Step Visual Reasoning
Zezhong Qian, Xiaowei Chi, Chak-Wing Mak et al. (+9 more)
Video models are evolving into
vision foundation models, yet they still lack human-like multi-step
reasoning. Streaming autoregressive
diffusion models are efficient but limited in
reasoning, while bidirectional
diffusion enables global revision with high inference costs due to dense frame-level denoising. Both paradigms struggle to achieve logical consistency and low-latency streaming for complex
reasoning tasks. We propose HDR (Hierarchical Denoising for Visual
Reasoning), a unified framework that integrates hierarchical latents into causal
video generation for multi-step
reasoning. HDR organizes
video latents into a tree-structured hierarchy, enabling coarse-to-fine
reasoning before streaming output. Coarse denoising layers preserve uncertain hypotheses for global planning, while finer layers progressively refine them into concrete visual states. A sparse hierarchical
attention pattern (SHAP) further reduces temporal
attention costs. We introduce a level-stratified multi-step
video reasoning benchmark with out-of-distribution cases, covering six tasks: maze
navigation, Tower of Hanoi, one-line drawing, sliding puzzle, Sokoban, and water pouring. Compared with streaming autoregressive
diffusion baselines, HDR improves success from 34.22 to 60.29 (76.2% relative gain) and increases average progress from 76.00 to 89.56, demonstrating more consistent
reasoning trajectories. HDR maintains low-latency streaming at 0.70 seconds per latent, achieving 54.2 times faster inference than bidirectional
diffusion. It also retains 82.9% of full-data performance with only 2% training data, compared with 52.0% for bidirectional
diffusion. Real-world
robot experiments further demonstrate HDR's potential for physical interaction and world modeling. Project demo: https://hierarchical-
diffusion-
reasoning.github.io/.
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All authors Zezhong Qian, Xiaowei Chi, Chak-Wing Mak, Tianze Zhou, Ruibin Yuan, Yuhan Rui, Hengzhe Sun, Zhuoqun Wu, Yuming Li, Siyuan Qian, Sirui Han, Shanghang Zhang
Computer Vision
MeanFlowNFT: Bringing Forward-Process RL to Average-Velocity Generators
Yushi Huang, Xiangxin Zhou, Jun Zhang et al. (+2 more)
MeanFlow generators achieve fast few-step sampling by predicting average velocities over time intervals, making them attractive for efficient generation. Reinforcement learning (RL) has become a powerful way to align
diffusion and flow models with human preferences and task-specific objectives. In particular, DiffusionNFT offers an efficient forward-process RL framework that does not require reverse-process trajectories or likelihood estimation. However, applying such RL methods to MeanFlow remains underexplored. DiffusionNFT optimizes instantaneous velocities, whereas MeanFlow samples with average velocities. To bridge this gap, we introduce MeanFlowNFT. Inspired by the MeanFlow identity, which bridges average and instantaneous velocities, we construct an induced instantaneous-velocity predictor. We apply the DiffusionNFT objective to this predictor, making reward optimization well-defined for MeanFlow. Sampling remains based on the average velocity, preserving MeanFlow's fast few-step generation. We further prove that MeanFlowNFT inherits DiffusionNFT's strict policy-improvement guarantee. Experiments on image and
video generation show that MeanFlowNFT consistently improves baselines. Moreover, it outperforms prior state-of-the-art RL-tuned few-step generators on most metrics ($6$ of $8$ on SD3.5-M), and can even surpass multi-step RL-tuned
diffusion while using only a few sampling steps. For instance, on Wan 2.1, $4$-step MeanFlowNFT reaches a VBench score of $84.33$, surpassing $50$-step LongCat-
Video RL ($82.57$).
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All authors Yushi Huang, Xiangxin Zhou, Jun Zhang, Liefeng Bo, Tianyu Pang
Computer Vision
Online Neural Space Time Memory for Dynamic Novel View Synthesis
Baback Elmieh, Lynn Tsai, Zeman Li et al. (+8 more)
Online novel view synthesis from multi-view streaming
videos faces a fundamental trade-off: maintaining a persistent, long-horizon
memory to reconstruct temporarily occluded regions while operating under strict real-time constraints. While Test-Time Training (TTT) offers a powerful
memory mechanism, standard models mandate gradient-based
memory updates at every frame to adapt to the changing motion in dynamic scenes. The computational cost of heavy
memory updates precludes real-time application and can lead to instability over long contexts. Given that
memory updates are more demanding than
memory application and
video content is largely redundant, we propose to decouple the frequencies of these two processes. Our approach performs periodic
memory updates while applying the
memory on a per-frame basis, using cross-view
attention to manage deformations between the prior
memory state and the current frame. To lock in the historical context, we introduce two critical mechanisms: an auxiliary
Memory Loss that forces persistent internalization of the scene, and a
Memory Caching strategy that regularizes active weights against catastrophic drift. Our method demonstrates state-of-the-art minute-scale
memory persistence in online dynamic human scenes at amortized real-time speed.
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All authors Baback Elmieh, Lynn Tsai, Zeman Li, Srinivas Kaza, Tiancheng Sun, Gabor Csapo, Ali Behrouz, Yuan Deng, Stephen Lombardi, Steven M. Seitz, Xuan Luo
Computer Vision
Motion-Conditioned Multi-View Fusion for Myocardial Infarction Localization from Echocardiography
Guang Yang, Wentian Xu, Siyu Wang et al. (+3 more)
Myocardial infarction (MI) remains a leading cause of mortality worldwide. Echocardiography (Echo) is a widely available modality for MI assessment, where regional wall motion abnormality is a key indicator. Prior learning based methods for myocardial motion analysis often use handcrafted descriptors or densely supervised estimation, but the need for extensive annotation limits applicability. Foundation models have recently improved
vision-based Echo analysis; however, most methods operate on single views and segment-level localization remains unreliable under view-dependent ambiguity, especially in apical views. To address this, we propose MCF-Net, a novel motion-guided multi-view fusion framework that fuses myocardial motion cues with foundation model representations to localize infarction. Visual features are extracted using EchoPrime, a pretrained Echo foundation model shared across dual views. Cardiac motion is modeled with extremely sparse supervision: a single annotated template frame is transferred across
videos to initialize point tracking, avoiding dense labels. Motion-derived segment-aware soft masks provide coarse spatial priors that selectively enhance features for challenging myocardial segments. A motion-conditioned fusion mechanism then integrates motion and
vision across views, refining predictions without overriding strong appearance cues. On segment-level MI localization, MCF-Net achieves 72.4% F1 and 84.9% accuracy, outperforming state-of-the-art motion-only,
vision-only, and fusion baselines.
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All authors Guang Yang, Wentian Xu, Siyu Wang, Betty Raman, Lei Li, Vicente Grau
Computer Vision
SceneBind: Binding What and Where Across Vision, Audio and Language
Mingfei Chen, Zijun Cui, Ruoke Zhang et al. (+2 more)
We present SceneBind, an omni-modal representation of realistic scenes with joint semantic and 3D spatial understanding across
vision, audio and
language. Existing omni-modal encoders excel at instance-level semantics (i.e., what is present), but often lack explicit spatial structure (i.e., where it is). SceneBind addresses this gap by representing each scene as a semantic-spatial entity, combining a global semantic
embedding with object-centric semantic-spatial slots. This representation explicitly captures object-level semantics, spatial attributes, and uncertainty. We further propose SceneBind Matching, a semantic-spatial matching scheme that integrates global scene similarity with object
alignment, supporting cross-modal scene
retrieval and object grounding. To train and evaluate SceneBind, we curate a novel real-world binaural audio-visual
dataset with structured semantic and spatial annotations, and propose a training protocol for aligning semantic and spatial signals across modalities. SceneBind is compatible with large-scale pretrained semantic encoders, adds lightweight spatial modeling with only a few additional
tokens. It achieves state-of-the-art scene and spatial
retrieval while enabling strong zero-shot transfer to downstream tasks such as audio-visual localization.
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All authors Mingfei Chen, Zijun Cui, Ruoke Zhang, Hyeonggon Ryu, Eli Shlizerman
Computer Vision
HoloGeo: Mitigating Landmark Bias in Geo-localization via Evidence-Driven Reasoning
Pengcheng Zhou, Xuanyu Liu, Yanchen Yin et al. (+4 more)
Recent advances in
Vision-
Language Models (VLMs) have significantly improved image geo-localization, yet existing models remain susceptible to landmark bias, causing them to overlook geographical cues or form spurious correlations, ultimately resulting in inaccurate localization. To systematically investigate this issue, we first design two quantitative metrics, Bias Intensity (BI) and Bias Harmfulness (BH), to characterize the impact of landmarks exerted on model
reasoning, and establish a comprehensive
benchmark, LandmarkBias-3K. To mitigate landmark bias, we further propose an evidence-driven
reasoning framework, HoloGeo, to improve the reliability of geo-localization. HoloGeo is supported by a high-quality
dataset, BF-30k, annotated with structured multi-evidence bias-free
reasoning chains. By incorporating multi-dimensional rewards, HoloGeo explicitly encourages balanced
attention over diverse visual cues and achieves evidence-driven joint
reasoning. Extensive experiments demonstrate that HoloGeo not only maintains excellent performance on IM2GPS3K and YFCC4k but also significantly outperforms existing open-source VLMs on LandmarkBias-3K, validating its effectiveness for robust geospatial
reasoning.
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All authors Pengcheng Zhou, Xuanyu Liu, Yanchen Yin, Bobo Li, Shengqiong Wu, Mong-Li Lee, Wynne Hsu
Computer Vision
ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors
Christos Korgialas, Gabriel Lee Jun Rong, Dion Jia Xu Ho et al. (+3 more)
The reliability of deepfake detectors frequently degrades under black-box
adversarial transfer, as these models often rely on fragile, architecture-dependent forensic cues. Existing transfer attacks often lack semantic awareness and struggle to maintain effectiveness under strict no-query constraints, particularly when perturbations are transferred from convolutional surrogates to
transformer-based targets. To address these limitations, this paper introduces ARMOR++, a robust multi-
agent framework designed for high-transferability deepfake evasion. The framework leverages the Qwen2.5-VL
Vision-
Language Model (VLM) to supply spatial semantic priors, while the Qwen3 Large
Language Model (LLM) orchestrates primitive selection, adaptive hyperparameter reparameterization, and
entropy-regularized perturbation mixing. By integrating five complementary primitives, spanning dense optimization, saliency-based methods, spatial transformations, frequency-domain perturbations, and block-structured modifications, ARMOR++ effectively targets heterogeneous inductive biases. Rigorous evaluation on the AADD-2025
benchmark demonstrates that ARMOR++ significantly outperforms existing agentic and non-agentic baselines across both low- and high-quality image regimes. Statistical analysis confirms a substantial gain in blind-target Attack Success Rate (ASR) over the state-of-the-art agentic baseline, with further performance advantages evidenced against non-agentic
benchmarks and under robust defensive configurations. These findings highlight a significant residual reliability gap in current deepfake detector deployments and demonstrate the efficacy of agentic orchestration in identifying latent vulnerabilities.
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All authors Christos Korgialas, Gabriel Lee Jun Rong, Dion Jia Xu Ho, Pai Chet Ng, Xiaoxiao Miao, Konstantinos N. Plataniotis
Computer Vision · Cross-listed from cs.CL
Beyond the Leaderboard: Design Lessons for Trustworthy Multimodal VQA
Sushant Gautam, Vajira Thambawita, Michael A. Riegler et al. (+2 more)
Healthcare
multimodal AI must combine visual and textual evidence while remaining reliable and interpretable. Using MediaEval Medico 2025 as a retrospective GI endoscopy case study, we analyze design choices across nine documented systems for question answering and explanation quality. Parameter-efficient adaptation of pretrained backbones provides strong challenge performance, but answer-level gains do not consistently translate into faithful and complete clinical
reasoning. Methods enforcing structured
reasoning and explicit grounding show more reliable behavior across heterogeneous question types, although the evidence is correlational rather than ablation-based. These results motivate evaluation beyond lexical overlap, standardized evidence-linked explanations, leakage-aware data governance, and lightweight robustness and calibration checks. The findings support trustworthy
multimodal healthcare AI based on data fusion, explainability, and resilient evaluation.
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All authors Sushant Gautam, Vajira Thambawita, Michael A. Riegler, Pål Halvorsen, Steven A. Hicks
Computer Vision
CRISP: Constrained Refinement via Iterative Squeezing Process for Robust Medical Image Segmentation under Domain Shift
Yizhou Fang, Pujin Cheng, Yixiang Liu et al. (+2 more)
Distribution shift in medical imaging remains a central bottleneck for the clinical translation of medical AI. Failure to address it can lead to severe performance degradation in unseen environments and exacerbate health inequities. Existing methods for domain adaptation are inherently limited by exhausting predefined possibilities through simulated shifts or pseudo-supervision. Such strategies struggle in the open-ended and unpredictable real world, where distribution shifts are effectively infinite. To address this challenge, we adopt the "Rank Stability of Positive Regions" as a working assumption under distribution shift, and use it to derive robust spatial hints for source-only
segmentation. Guided by this assumption, we propose CRISP, a model-agnostic framework that, unlike deployment-time adaptation, requires no test-time parameter updates and no target-domain data–a target-free, plug-in refinement framework that segments with frozen weights. Rather than using ranking to directly output masks, CRISP exploits the stability of probability rankings under distribution shift to derive robust spatial priors. Via latent feature perturbation, perturbation-invariant high-grade regions define a high-precision (HP) core, while voxels that remain potentially foreground under at least one perturbation define a high-recall (HR) support; these dual priors are then recursively refined under perturbation. We then design an iterative training framework that progressively squeezes HP and HR toward the final
segmentation. Extensive evaluations on multi-center cardiac MRI and CT-based lung vessel
segmentation demonstrate CRISP's superior robustness, significantly outperforming state-of-the-art methods with striking HD95 reductions of up to 0.14 (7.0% improvement), 1.90 (13.1% improvement), and 8.39 (38.9% improvement) pixels across multi-center, demographic, and modality shifts, respectively.
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All authors Yizhou Fang, Pujin Cheng, Yixiang Liu, Xiaoying Tang, Longxi Zhou
Computer Vision
Divergent Gaze Patterns in Artistic Viewing: Spatial and Temporal Signatures of Attention Across Autistic Individuals, Artists, and Neurotypical Observers
Mohammed Amine Kerkouri, Daphné Senggaran, Renaud Jusiak et al. (+7 more)
How different populations visually explore artworks bears on cognitive science and on accessibility design, yet most eye-tracking work in autism has used social scenes rather than art, and has analysed where the eyes land while ignoring when and in what order. We present a comparative free-viewing study across three groups, autistic adults (ASD), trained artists, and neurotypical observers, who each viewed 30 paintings for 15s. We introduce a directed, metric-grounded framework that compares groups along two complementary axes: a spatial axis, in which one group's fixation-density map predicts another's fixations under six saliency metrics (AUC-Judd, NSS, CC, SIM, KL, Information Gain); and a temporal axis, in which individual scanpaths are compared with MultiMatch, ScanMatch, a foveal-disc IoU score (FDISS), and dynamic time warping (DTW). Fixations are extracted uniformly for all groups with a dispersion-threshold
algorithm. Three results converge. (i)Artists and neurotypicals are almost indistinguishable in both space (density-map correlation CC=0.96) and time (they form the most alignable scanpath pair), whereas ASD gaze diverges from both. (ii)ASD
attention is dissociated: it matches artists' wide spatial exploration (dispersion, explored area) but carries a distinct temporal signature, shorter fixations, less dwell, and the most idiosyncratic (least self-consistent) scanpaths of any group. (iii)ASD gaze is not selectively artist-like on any metric; if anything it is marginally closer to neurotypical. Together these findings indicate that autistic viewing of art is a distinct, group-specific attentional profile in both space and time, and they motivate population-conditioned models of aesthetic
attention. We release all analysis code and per-stimulus results.
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All authors Mohammed Amine Kerkouri, Daphné Senggaran, Renaud Jusiak, Océane Lehmann, Marouane Tliba, Claire Wardak, Emmanuelle Houy-Durand, Shasha Morel-Kohlmeyer, Aladine Chetouani, Nadia Aguillon-Hernandez
Computer Vision
Structural-Semantic Reciprocal Learning for Unsupervised Visible-Infrared Person Re-Identification
Moyao Tian, Shijia Liu, Yan Yang et al. (+4 more)
Unsupervised visible-infrared person re-identification (USVI-ReID) is challenging due to the large modality gap and the lack of cross-modal identity annotations. Progressive association paradigms have been proposed to gradually bridge the gap, but they suffer from two critical bottlenecks: reliance on ambiguous global representations and unchecked propagation of pseudo-label noise in an open-loop manner. To address these issues, we propose Structural-Semantic Reciprocal Learning (SSRL), a framework that transforms open-loop association into a self-correcting closed-loop system. Structurally, we introduce Fine-grained Structural Decoupling (FSD) to extract discriminative body-part primitives as reliable spatial anchors, complementing ambiguous holistic silhouettes with spatially consistent structural details. Semantically, we design a Closed-loop Semantic Calibration (CSC) mechanism that reconstructs shared semantic prototypes at each epoch and feeds them back into the training loop, effectively filtering pseudo-label noise before the next clustering cycle. Through the reciprocal interaction between structural and semantic learning, SSRL achieves robust cross-modal representation. Extensive experiments demonstrate the competitive performance of SSRL against state-of-the-art USVI-ReID methods on both SYSU-MM01 and RegDB, notably surpassing several supervised counterparts on RegDB.
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All authors Moyao Tian, Shijia Liu, Yan Yang, Xin Yuan, Minshi Chen, Wei Wang, Xiao Wang
Computer Vision
Symbal: Detecting Systematic Misalignments in Model-Generated Captions
Maya Varma, Jean-Benoit Delbrouck, Sophie Ostmeier et al. (+2 more)
Multimodal large
language models (MLLMs) often introduce errors when generating image captions, resulting in misaligned image-text pairs. Our work focuses on a class of captioning errors that we refer to as systematic misalignments, where a recurring error in MLLM-generated captions is closely associated with the presence of a specific visual feature in the paired image. Given a
vision-
language dataset with MLLM-generated captions, our aim in this work is to detect such errors, a task we refer to as systematic misalignment detection. As our first key contribution, we present Symbal, which utilizes a structured, dual-stage setup with off-the-shelf foundation models to identify systematic misalignments and summarize results in natural
language. As our second key contribution, we introduce SymbalBench, a
benchmark designed to evaluate automated methods on our proposed task. SymbalBench consists of 1.7 million image-text pairs from two domains (natural and medical images), organized into 420
vision-
language datasets with annotated systematic misalignments. Symbal exhibits strong performance on this
benchmark, correctly identifying systematic misalignments in 63.8% of
datasets, a nearly 4x improvement over the closest baseline. We supplement our evaluations on SymbalBench with real-world evaluations, showing that (1) Symbal can accurately surface systematic misalignments in captions generated by four MLLMs and (2) Symbal is a powerful tool for
auditing off-the-shelf image-caption
datasets. Ultimately, our novel task, method, and
benchmark can aid users with
auditing MLLM-generated captions and identifying critical errors, without requiring access to the underlying MLLM. Code is available at https://github.com/Stanford-AIMI/Symbal.
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All authors Maya Varma, Jean-Benoit Delbrouck, Sophie Ostmeier, Akshay Chaudhari, Curtis Langlotz
Computer Vision
MAGiSt3R: Multi-Agent Feed-forward 3D Reconstruction from Monocular RGB Videos
Ziren Gong, Xiaohan Li, Fabio Tosi et al. (+4 more)
This paper presents MAGiSt3R, a multi-
agent 3D reconstruction framework performing reconstruction and camera tracking for
monocular RGB
videos at almost 10 FPS. MAGiSt3R relies on a feed-forward model from the 3R family to process RGB
videos and regress local point maps, and on a merging model, MAGMA, that combines local maps at both intra-
agent and inter-
agent levels to obtain the final global point map. Furthermore, MAGiSt3R performs pose graph optimization to mitigate cumulative camera drift occurring along the feed-forward pipeline. We evaluate MAGiSt3R on both synthetic and real-world
datasets, demonstrating its superior reconstruction and camera tracking accuracy compared to state-of-the-art approaches.
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All authors Ziren Gong, Xiaohan Li, Fabio Tosi, Ninghui Xu, Stefano Mattoccia, Jianfei Cai, Matteo Poggi
Computer Vision
Ray-based phase error correction for miniaturized DOE projector-based FPP under single-directional hyperbolic projection
Seung-Jae Son, Yatong An and Jae-Sang Hyun
Fringe Projection Profilometry (FPP) systems using miniaturized DOE pro-jectors often suffer from severe phase artifacts due to nonlinear projection characteristics and limited pattern controllability. We propose a ray-based phase error correction framework that models phase artifacts along projection rays from the projector pinhole, incorporating projector geometry without re-lying on image-domain processing or neighboring pixels. A projector pinhole estimation method based on a single-directional
hyperbolic fringe pattern is introduced, through which projector geometry can be recovered without stereo calibration. In addition, a data-efficient strategy constructs the re-finement model from a single calibration pose. Experiments on miniaturized DOE projector-based FPP systems demonstrate significant improvements in reconstruction accuracy under nonlinear projection conditions, confirming the robustness and physical consistency of the proposed approach.
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Computer Vision
DAPGNet: Dynamic Adaptive Physics-Guided Graph Diffusion Network for Hyperspectral Image Classification
Pengkun Wang, Weijia Cao, Ning Wang et al. (+1 more)
Hyperspectral image (HSI) classification requires reliable pixel-relation modeling under spectral variability, mixed pixels, and heterogeneous boundaries. Existing graph-based HSI classifiers usually construct graph
topology from spatial proximity, superpixel connectivity, or learned feature affinity. However, the spectral physical prior carried by contiguous bands has limited influence on
topology estimation and message propagation. This paper presents DAPGNet, a dynamic adaptive physics-guided graph
diffusion network that injects a structure-constrained physical prior into relation-level graph learning. DAPGNet first encodes contiguous spectral responses into node-wise multiscale physical-prior representations. A two-stage graph constructor then combines spectral-spatial affinity, physical-prior consistency, and spatial distance to form a physical-prior-aware sparse
topology. During graph
diffusion, learned edge weights are transformed into additive
attention biases, while a physical gate performs node-wise and feature-wise interpolation between graph-aggregated features and projected physical-prior features. Cross-scale fusion integrates node states from different
diffusion depths, and the network is optimized with main classification, auxiliary supervision, and second-order spectral smoothness regularization. Experiments on Indian Pines, WHU-Hi-LongKou, Houston2013, and Houston2018 show that DAPGNet achieves the best OA, AA, and Kappa among representative CNN-,
Transformer-, Mamba-, and graph-based baselines. It improves AA over the strongest competing method by 3.64 to 7.31 percentage points across the four
datasets. Ablation and sensitivity analyses further support the complementary effects of physical-prior extraction, prior-aware
topology construction, physics-gated propagation, and spectral smoothness regularization.
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All authors Pengkun Wang, Weijia Cao, Ning Wang, Xiaofei Yang
Computer Vision
QuReC: All-in-One Image Restoration with Query-Specific Guidance and Local-Global Response Calibration
Shen Zhou, Jinghui Zhang, Wenbo Huang et al. (+7 more)
All-in-one image restoration aims to recover clean images degraded by multiple corruption types using a single unified model. Existing methods typically rely on image-level
prompts or shared guidance to handle diverse degradations. However, such a paradigm becomes inadequate when degradations are spatially heterogeneous or even coexist in mixed forms within a single image. Yet spatially adaptive guidance alone is not sufficient, since accurate restoration also requires each spatial query to reliably aggregate complementary information from local neighborhoods and global contexts. To this end, we propose QuReC, a unified framework for all-in-one image restoration. QuReC consists of a Degradation-Guided Query Reconstruction Module (DQRM) and a Local-Global Response Calibration Module (LGRCM). Specifically, DQRM matches each spatial query against a degradation prototype space to reconstruct a query-specific degradation-aware representation, thereby providing fine-grained spatially adaptive restoration guidance. To further stabilize this query-wise matching process, we introduce a weakly supervised prototype matching learning strategy to improve optimization stability and degradation semantic consistency. Meanwhile, LGRCM performs local-global dual-branch aggregation and calibrates the aggregated responses with learnable priors, improving the reliability of feature aggregation and the coordination between local detail modeling and global context modeling. Extensive experiments demonstrate that QuReC achieves superior performance on multiple all-in-one image restoration
benchmarks. The code is released at https://github.com/zhoushen1/QuReC.
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All authors Shen Zhou, Jinghui Zhang, Wenbo Huang, Xuwei Qian, Zhen Wu, Guangwen Peng, Zhiyuan Li, Ding Ding, Dian Shen, Fang Dong
Computer Vision
AlphaWiSE: Adaptive Weight Interpolation for Continual Multimodal Representation Learning
Sarthak Jain, Qiran Hu, Zhen Zhu et al. (+1 more)
Multimodal models such as CLIP learn a shared
embedding space for cross-modal
retrieval, but continual adaptation to sequentially arriving data can disrupt the cross-modal
alignment acquired from earlier phases. Conventional continual-learning methods return a single checkpoint, which commits every
retrieval direction to the same stability-plasticity trade-off. We propose AlphaWiSE, a post-hoc weight-space interpolation method that composes two frozen source checkpoints. For each aligned parameter tensor identified by its checkpoint key, AlphaWiSE fits one scalar interpolation coefficient shared by all tensor entries. The coefficients are fitted on a smaller exemplar
memory and used to materialize one interpolated checkpoint. The deployed model has the same architecture and parameter count as either source checkpoint, which does not require additional inference time. Extensive experiments on audio-image-text
retrieval show consistent improvements over strong continual-learning baselines across multiple
retrieval directions and evaluation metrics.
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All authors Sarthak Jain, Qiran Hu, Zhen Zhu, Yaoyao Liu
Computer Vision
Quantifying Training Membership Information in the Hyperspherical Embedding Geometry of Face Recognition Models
Ünsal Öztürk and Sébastien Marcel
Face recognition models represent each face as an
embedding vector on the unit hypersphere by clustering
embeddings of the same identity while pushing different identities apart through angular-margin losses. Because these losses act only on training identities, non-member identities may form clusters with different
geometric properties. In this paper, we quantify the magnitude of this difference and what training-time factors control it. We compute four statistics based on cluster geometry across 180 face recognition models in a factorial design over IResNet backbone size, loss head, training duration, and the number of training identities, and evaluate each configuration on nine
benchmarks. Our results indicate that the number of training identities has the largest effect on member/non-member separability, while backbone and loss head contribute far less, and that, on a same-domain held-out reference, the
geometric membership signal decreases monotonically as more identities are added to training. We provide an analysis of cross-domain (pose, age, quality, ethnicity) non-member
benchmarks and report that these inflate the apparent membership signal. Finally, we fuse all four statistics with a learned classifier to reveal additional membership information beyond the best individual statistic.
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Computer Vision
Towards Hierarchical Structure Understanding of Newspaper Images
William Mocaër, Solène Tarride, Thomas Constum et al. (+7 more)
Understanding newspaper images remains a challenging task due to their complex, nested hierarchical structures and dense, heterogeneous layouts. In this paper, we explore two complementary approaches for newspaper structure understanding. First, we present a modular bottom-up pipeline that combines state-of-the-art open-source models: YOLO for layout detection, LayoutReader for reading order prediction, and a custom
algorithm for article
segmentation. This approach leverages existing robust components while maintaining flexibility and interpretability. Second, we introduce Tiramisu (Tiered
Transformers for Hierarchical Structure Understanding), a novel end-to-end
transformer-based architecture that explicitly models document hierarchy through an iterative tiered process. Tiramisu performs section and article separation, block localization, semantic categorization, and reading order prediction using highly parallelized
attention mechanisms. Finally, we release Finlam La Liberté, a new
dataset designed specifically for evaluating hierarchical information
retrieval in historical newspapers. Experimental results demonstrate the effectiveness of both approaches in reconstructing complex newspaper hierarchies, with comparative analysis highlighting their respective strengths for scalable document digitization. The Tiramisu training code, including the synthetic newspaper generator, is available at https://git.litislab.fr/tiramisu/tiramisu-newspaper-articles-extractor.
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All authors William Mocaër, Solène Tarride, Thomas Constum, Merveilles Agbeti-Messan, Tom Simon, Clément Chatelain, Stéphane Nicolas, Pierrick Tranouez, Sébastien Cretin, Thierry Paquet
Computer Vision · Cross-listed from eess.IV
ESAR: Event-Based Synthetic Aperture Reconstruction
Harbir Antil, Daniel Blauvelt and David Sayre
Event cameras report asynchronous polarity events when changes in log–radiance exceed a fixed contrast threshold, producing signed temporal contrast measurements rather than conventional image frames. We formulate
monocular event-based imaging as a synthetic-aperture inverse problem for a static ground-domain log–radiance field $\theta \in \mathbb{R}^{N_g}$. Instead of reconstructing a latent pixel-time volume $v \in \mathbb{R}^{N_pN_t}$, we impose the
geometric relation $v=P\theta$, where $P$ maps the fixed scene into motion-dependent latent views. Aggregating events over finite time intervals gives the linearized model \[ AP\theta = b+\eta, \] where $A$ is a temporal differencing operator, $b$ contains signed binned event counts, and $\eta$ represents measurement and modeling errors. This decomposition exposes a synthetic-aperture structure: under near-nadir motion, successive projections are approximately shifted views of a common scene, while the composite operator $AP$ remains ill-conditioned because it combines spatial averaging with temporal differencing. We therefore use regularized inversion to recover $\theta$. Numerical experiments on simulated data and real near-nadir Falcon Neuro event data show that the proposed $\theta$-based formulation recovers coherent large-scale spatial structure, relative to dynamic latent-image and learned event-reconstruction baselines, while suppressing fine-scale texture.
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Computer Vision · Cross-listed from cs.RO
DriftWorld: Fast World Modeling through Drifting
Susie Lu, Haonan Chen, Weirui Ye et al. (+1 more)
Predictive world models enable
robots to simulate the visual outcomes of their actions, but state-of-the-art
diffusion-based models remain costly because generating each rollout requires multi-step iterative denoising. We introduce DriftWorld, an action-conditioned world model based on drifting generative models. DriftWorld learns a conditional drift during training, enabling it to generate future observations for a given action sequence in a single forward pass during inference. Across Bridge-V2, RT-1,
Language Table, Push-T, and Robomimic, DriftWorld runs at over 40 fps and is 12+ times faster than
diffusion-based baselines, while matching or improving their visual generation quality. This makes DriftWorld an efficient world model for
robot simulation and further enables downstream applications including inference-time action search and offline policy evaluation.
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All authors Susie Lu, Haonan Chen, Weirui Ye, Yilun Du
Computer Vision
SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment
Saad Ejaz, Miguel Fernandez-Cortizas, Javier Civera et al. (+2 more)
CAD-to-image
alignment aims to estimate an object's 9D pose (rotation, translation, and anisotropic scale) from a single RGB image, with applications in robotics and augmented reality. Recent zero-shot methods use
vision foundation models to match image regions to CAD models; yet their correspondences are typically appearance-driven or unreliable under occlusion or synthetic-to-real domain shift. To address these limitations, we introduce SUFLECA (Scaling Up Feature LEarning for CAD-to-image
Alignment), a weakly supervised framework for zero-shot CAD
alignment with two key contributions. First, SUFLECA scales up geometry-grounded feature learning from pretrained visual representations through Normalized Object Coordinates (NOCs) supervision on images spanning up to 12 real and synthetic
datasets, learning compact geometry-aware features that generalize across domains. Second, we propose a geometrically consistent matching
algorithm that establishes reliable CAD-to-image correspondences. Together, these contributions enable accurate, sub-second
alignment per object instance without iterative pose refinement. On ScanNet25k, SUFLECA achieves 32.8%/42.6% category/instance accuracy, outperforming the strongest zero-shot baseline by 9.7/12.5 percentage points with a smaller computational footprint, and for the first time on this
benchmark, even surpassing existing pose-supervised methods. Code is available at: https://github.com/snt-arg/SUFLECA
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All authors Saad Ejaz, Miguel Fernandez-Cortizas, Javier Civera, Holger Voos, Jose Luis Sanchez-Lopez
Computer Vision
Beyond Single Expert: Harmonizing Diverse Visual Priors in MLLMs for Spatial Understanding
Xiao Lin, Xiaohu Huang and Kai Han
Multimodal Large
Language Models (MLLMs) have demonstrated substantial promise in spatial understanding. Existing works typically incorporate prior knowledge extracted from a pre-trained foundation model to further enhance the spatial awareness of MLLMs. In this paper, we first reveal that when integrating diverse foundation models into MLLMs, different models provide complementary spatial priors that benefit different tasks. Motivated by this, we propose $\textbf{ViPS}$, a novel multi-model prior framework designed to fully unleash the potential of incorporating multiple $\textbf{Vi}$sual $\textbf{P}$riors from diverse models into MLLMs for $\textbf{S}$patial understanding. Specifically, ViPS introduces an Efficient Prior Proxy to generate multiple foundational priors with minimal inference overhead, and a Dynamic Prior Fusion mechanism to achieve harmonious and context-aware prior fusion and injection from the prior proxies. Extensive experiments demonstrate that ViPS successfully harmonizes diverse visual priors, establishing new state-of-the-art performance across multiple complex spatial
reasoning and 3D spatial understanding
benchmarks. Project page: https://visual-ai.github.io/vips
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