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Computer vision

Perception from images and video: detection, segmentation, tracking, 3D reconstruction, medical and remote imaging.

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All fields3294Multimodal499Agents491Frontier LLMs423Inference & serving246Learning verifiable rewards164Image segmentation161Graph learning1583D scene140Evals108Voice AI88Flow matching75Forecasting time75Classifier & causal discovery75Training & fine-tuning64Teacher policy distillation55Computer vision51All 32 fields
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Papers

Open problems in Computer vision

  • View-dependent geometry features in 3D world models1 paper raises it · 1 addresses it

    Entangles scene dynamics with camera motion, making state prediction harder and less generalizable.

    • GeoState: Learning View-Decoupled Geometry States for 3D World Modelingraises · addresses
  • High-quality 3D Gaussian rendering under large viewpoint changes1 paper raises it · 1 addresses it

    Feed-forward methods struggle to learn Gaussian attributes for consistent rendering across extreme viewpoints.

    • InfiniSplat v2: Analyzing and Improving Feed-Forward 3DGS from Any Viewsraises · addresses
  • Dense 4D human correspondence without templates1 paper raises it · 1 addresses it

    Existing methods fail to track surface points across articulation, deformation, and occlusion without relying on body templates.

    • Dense4D: Pixel-Level 4D Human Correspondenceraises · addresses
  • Open-set human activity and attribute description in videos1 paper raises it · 1 addresses it

    Current datasets and models simplify to closed-set or single-person tasks, limiting real-world applicability.

    • Grounded Human-Attributed Description and Activity Recognition in Videosraises · addresses
  • Annotation-free medical image segmentation1 paper raises it · 1 addresses it

    Manual annotations for tasks like retinal vessel segmentation are time-consuming and require expert knowledge.

    • FPG-Seg: Annotation-free Retinal Vessel Topology Segmentationraises · addresses

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  • Sensing communication16+23.1%
  • Federated learning40+14.3%
  • Agents491−2.2%
  • Frontier LLMs423−9.8%
  • Forecasting time75−16.7%
  • Learning verifiable rewards164−17.6%
  • Inference & serving246−18.3%
  • Character recognition OCR15−21.1%

Papers

  • arXiv cs.CVMultimodalOct 15▲ 6

    Gen4P: Video Generators are 4D Perceivers

    Yitong Xia, Zeren Jiang, Chuanxia Zheng

    We introduce Gen4P, a unified framework for recovering 4D geometry and motion from monocular video. We show that a pretrained video generator can be repurposed into a feed-forward 4D perceiver without iterative denoising. Building on on-demand querying, Gen4P processes clean video latents in a single encoder pass and predicts the requested 4D attributes through a lightweight cross-attention decoder. The on-demand query formulation enables flexible spatial sampling, and our proposed continuous-time queries further provide temporal flexibility. To strengthen geometric knowledge, we propose a straightforward plug-in solution by incorporating a pretrained video-depth LoRA as a frozen geometry prior, improving overall performance without adding extra trainable parameters. Gen4P achieves state-of-the-art or competitive performance on tasks of 3D point tracking, video depth and camera pose estimation. Qualitative results on in-the-wild videos illustrate its capability of tracking complex motion while maintaining the delicate structure. Together, these results demonstrate the potential of video generative pretraining for unified 4D perception. See https://gen4p.github.io for more visual…

    AIComputer vision80CodePDF
  • arXiv cs.CVComputer visionOct 15▲ 2

    GeoState: Learning View-Decoupled Geometry States for 3D World Modeling

    Hongchang Liu, Jian Liu, Yujuan Ding, Liang Peng, Xun Jiang +3

    Recent 3D world models increasingly use features from geometry foundation models (GFMs) as predictive world states, benefiting from their rich geometric priors. However, the view dependence of these features entangles scene dynamics with camera-induced variation, making the resulting state evolution harder to predict. We propose GeoState, a view-decoupled geometry representation for predictive 3D world modeling that separates scene-state evolution from camera-dependent observation. Specifically, a scene tokenizer aggregates multi-view GFM features into a view-decoupled scene state, which a geometry decoder queries using camera rays. An autoregressive world model then predicts future scene states, while camera motion is modeled separately. The predicted states can therefore be decoded under either predicted or externally specified cameras, while retaining a single context-derived scale throughout the rollout without per-frame scale alignment. Experiments show that GeoState outperforms prior 3D world models on Waymo, supports geometrically consistent cross-view querying, and generalizes zero-shot to KITTI and nuScenes. Project page:…

    AI60Code
  • arXiv cs.CVComputer visionOct 15

    Dense4D: Pixel-Level 4D Human Correspondence

    Rıza Alp Güler

    Reconstructing human motion in 4D requires identifying the same surface points through articulation, deformation and changes in visibility. We introduce Dense4D, to our knowledge the first feed-forward model dedicated to dense 4D correspondence across the body, clothing and hair from a set of monocular RGB images, without a body template. Trained specifically for human geometry, Dense4D jointly processes person-centred crops with known image-to-crop transforms to predict Human Point Maps (HPMs) and depth for each image. An HPM assigns each person pixel the 3D position of its observed surface point at a common reference time. These maps support amodal correspondence: points hidden in the reference image can be localised from observations in other frames. Matching HPM coordinates across crops establishes correspondence without predicting camera poses or large scene displacements; per-image depth then lifts the matches into 3D tracks. Experiments on real people demonstrate substantially higher tracking accuracy and closer agreement with scanned surfaces than the evaluated baselines. Points from different frames assemble into coherent human geometry in the reference pose, providing…

    AI60PDF
  • arXiv cs.CVComputer visionOct 15▲ 2

    ARC-SORT: Adaptive, Robust, Compensated Multi-Object Tracking with Interacting Multiple Models

    Girish Kumar Adari

    Multi-object tracking in sports broadcasts is hard for two reasons: athletes move fast and change direction abruptly, and the camera pans and zooms at the same time. We present ARC-SORT (Adaptive, Robust, Compensated SORT), a tracking-by-detection tracker that trains nothing and runs in real time. Its design rule: the tracker measures its own uncertainty, and that measurement decides how it acts. An Interacting Multiple Model (IMM) Kalman filter gives every track a predictive covariance, and we turn it into a per-track search region: the less reliable the motion prediction, the larger the region in which a detection may match (uncertainty-adaptive Expansion IoU, EIoU). On identical inputs, this continuous coupling is what matters: the same covariance used as a hard Mahalanobis gate hurts at every threshold we swept, and richer covariance summaries add nothing measurable. When two tracks cross, the assignment cost itself reports whether the decision was close. ARC-SORT keeps such pairs under review and applies at most one corrective identity swap once post-crossing appearance evidence shows the first decision was wrong; this adds +1.0 HOTA (higher order tracking accuracy) on both…

    AI50Code
  • arXiv cs.CVComputer visionOct 15

    Grounded Human-Attributed Description and Activity Recognition in Videos

    Jay N. Paranjape, Jurijs Nazarovs, Rohan Sarkar, Yue Guo, Lipeng Ke +3

    Understanding characteristics and activities of individuals in complex multi-person environments is crucial for real-world applications. However, existing datasets often simplify this problem to grounded group activity recognition, single-person activity recognition, or close-set activity recognition, thus limiting the generalizability of models trained on these datasets. In this work, we introduce the task of Grounded Human-Attributed Description and Activity Recognition (GHADAR), which involves describing the characteristics and activity descriptions of \textbf{every} person in a video, provided the location of the person in the video, which is more practical for real-world applications. To facilitate this, we introduce a new dataset derived from AVA-Actions by generating open-set captions for the person description and activity. In addition, we propose a novel method to effectively utilize the information contained in grounding during training by constraining the cross-attention masks during training in VLMs to improve performance for this task. Our experiments show that our method outperforms SOTA VLMs on this task. Finally, we demonstrate the limitations of existing…

    AIMultimodal60PDF
  • arXiv cs.CVEvalsOct 15

    FPG-Seg: Annotation-free Retinal Vessel Topology Segmentation

    James Willoughby, Irina Voiculescu

    State-of-the art approaches to retinal vessel segmentation typically require extensive annotations, a highly time-consuming process, reliant on clinical expertise. We present FPG-Seg, an annotation-free pipeline for retinal vessel segmentation combining classical vesselness filtering, Bayesian deep learning and topology-aware graph reconstruction to segment retinal vessel topology without manual labels. Tested across four widely used benchmark datasets for retinal vessel segmentation, FPG-Seg attains a clDice of $0.74{-}0.78$, representing $86{-}98\%$ of the performance of an equivalent fully-supervised baseline, despite using no manual annotations for training or hyperparameter tuning. As part of this work, we also provide, to our knowledge, the first performance benchmark of an annotation-free vessel segmentation method on FIVES.

    AIComputer vision50PDF
  • arXiv cs.CVComputer visionOct 15

    TRACE: Topology-Aware Reconnection via Prediction-Adaptive Connectivity Enforcement for Thin-Structure Segmentation

    Junho Moon, Ikbeom Jang

    Thin, elongated structures are particularly sensitive to connectivity errors, as even a few local segmentation mistakes can disrupt otherwise continuous paths. Existing skeleton-based objectives typically supervise fixed centerline locations and do not account for alternative local routes that may restore connectivity. We propose TRACE, a topology-aware, prediction-adaptive objective that uses a target skeleton to define required local connections. For each connection, TRACE identifies the easiest valid reconnection under the current prediction and jointly supervises its bottleneck with the corresponding skeleton point. The supervision thus adapts to the evolving prediction rather than remaining fixed to the centerline. We further introduce a topology-aware skeleton carrier that preserves both foreground and class-specific topology, enabling reliable definition of these connections. Extensive experiments across six 2D and 3D benchmarks show that TRACE improves connectivity and topology while maintaining strong segmentation accuracy with low training overhead. Code will be released upon acceptance.

    AI50PDF
  • arXiv cs.CVAgentsOct 15▲ 316

    OpenGCUA: Toward a Fully Open Stack for Generalist Computer Use

    Hanzhang Zhou, Long Li, Quyu Kong, Lei Wang, Panrong Tong +10

    Computer use has reached a turning point: the focus is expanding from GUI-only interaction to systems that complete tasks on users' computers through visual and programmatic interfaces. We define a generalist computer-use agent (GCUA) as a system in which the model chooses how to observe, act, and organize its work across visual and programmatic interfaces. Yet frontier implementations, such as GPT-6 Astra's computer-use system, remain largely closed, limiting systematic study of their design. We introduce Open Generalist CUA (OpenGCUA), an ongoing project to develop and open the full stack for GCUAs with the community. This report presents the first release of OpenGCUA. Code execution is a key feature of our agent design: it lets intermediate results determine subsequent operations and enables models to apply their existing programming capabilities directly to computer interaction. We also propose harness-to-model decision transfer, moving selected observations and task-state decisions from fixed harness rules to the model. On OSWorld-v2, our full configuration improves task scores across all eight evaluated models, including GPT-6 Astra and Claude Opus 5, raising mean partial…

    AIComputer vision80Code
  • arXiv cs.CVComputer visionOct 15

    Motion- and Gravity-Aware Feedforward 3D Reconstruction

    Shiyao Qian, Yuan Ren, Dongfeng Bai

    Feed-forward transformer models have recently achieved strong performance in multi-view 3D reconstruction, but they still rely primarily on appearance-based attention and often struggle in dynamic scenes with weak geometric cues. In this work, we introduce a motion- and gravity-aware framework under the Learning Using Privileged Information (LUPI) paradigm. During training, residual optical flow and gravity direction are provided as privileged signals to guide geometric representation learning; during inference, the model uses standard visual inputs only. We integrate these priors into transformer backbones with lightweight architectural changes, preserving feed-forward efficiency. Experiments on standard benchmarks show consistent gains in reconstruction accuracy and robustness, especially in scenes with dynamic objects, ambiguous structure, and challenging camera motion. Our analysis further shows that the optimal injection stage is backbone-dependent: middle-layer integration is generally preferable for VGGT, while late-stage integration is more effective for π3. These results suggest that LUPI offers a practical way to inject motion and physical priors into feed-forward 3D…

    AI60PDF
  • arXiv cs.LGComputer visionOct 15

    Scene Reconstruction Using Ubiquitous Wireless Signals via Diffusion Posterior Inference Over physics-Grounded Representations

    Vahid Yazdnian, Yasaman Ghasempour

    Knowing the layout of the physical world matters for many applications, yet the sensors we rely on for it (e.g., cameras and LiDAR) can fail in darkness, in adverse weather, or behind walls. Wireless signals do not face these limitations: every link between a base station and a user is shaped by the buildings it passes, reflects from, or is blocked by, leaving information about the surrounding scene embedded in the wireless channel measured at the endpoints. However, mapping channel measurements to the environment is a severely ill-posed problem: a measurement records only the structures that interact with the transmitted electromagnetic waves along its propagation paths, and even these observations can often be explained by multiple plausible scenes. We therefore formulate wireless scene reconstruction as posterior inference and develop a hierarchical framework with two complementary stages to approach this problem. A wireless foundation model, pretrained to respect propagation physics, transforms each channel measurement into a representation of the scene structure that shaped the observation. A diffusion model then jointly integrates representations from multiple measurements…

    AIMultimodal70PDF
  • arXiv cs.CVComputer visionOct 15

    Data-Efficient ConvNeXt Pretraining for High-Resolution Sentinel-1 GRD SAR Representation Learning

    Yuki Natsume, Sang-Ho Yun, Chai Kiat Yeo

    Labeled Sentinel-1 Ground Range Detected (S1GRD) datasets remain scarce, limiting supervised deep learning for land-surface remote sensing, while self-supervised pretraining for this domain has been dominated by Vision Transformer (ViT)-based masked image modeling requiring substantial data and compute. We propose SparK-SAR, a sparse convolutional masked pretraining framework built on SparK and ConvNeXt, designed to exploit convolutional inductive biases for S1GRD imagery under constrained compute. SparK-SAR makes three core technical contributions: (i) ConvNeXt-RS, a domain-adapted encoder that halves the stem stride (4x to 2x) to produce 14x14 feature maps at stride 16x, providing 4x more spatial tokens at identical parameter count; (ii) a lightweight ConvNeXt-512 reconstruction decoder with PixelShuffle upsampling that reduces pretraining VRAM by 7.5-8.5% per GPU relative to a UNet-128 baseline while matching its accuracy; and (iii) an empirical characterization of FP16 mixed-precision pretraining instability in the standard SparK UNet-768 decoder on S1GRD data, complemented by a finding that finetuning with layer-wise learning rate decay (LLRD) improves downstream performance…

    AI50PDF
  • arXiv cs.CVComputer visionOct 15

    VesperMUSE: Multi-Modal Fusion Vision Transformer for Image Semantic Segmentation Under Diverse Light Conditions

    Raul Balmez

    Semantic segmentation is critical for the reliable navigation and scene understanding of autonomous systems, yet its performance degrades severely in low-light environments due to noise, artifacts, colour distortion, and the loss of structural details. To address this challenge, this work introduces VesperMUSE, a unified multi-modal framework designed to achieve robust image semantic segmentation across diverse illumination conditions. The architecture addresses the light deficiency problem on multiple levels: proposing a novel physics-informed synthetic low-light adaptation algorithm, approaching Low-Light Image Enhancement (LLIE) through a thermal-guided paradigm, and finally aiming to recover the intrinsic scene details for semantic segmentation through a multi-modal fusion approach. Following enhancement, the framework dynamically integrates the primary RGB features with auxiliary thermal representations and surface normal maps highlighting geometric discontinuities and material boundaries. At the core of this integration is a highly scalable, modality-agnostic self-attention mechanism that extracts both global context and fine local features from an arbitrary number of…

    AI60PDF
  • arXiv cs.LGComputer visionOct 15

    P2TPO: Structure-Aligned Page-to-Token Credit Assignment for Full-Page Document Parsing

    Yong Zu, Ninghua Yang, Zezhong Tan, Xinhong Ma, Ziqiang Dong +1

    End-to-end full-page document parsing must preserve the content and structure of heterogeneous elements, yet otherwise accurate outputs can contain local errors and omissions. A single page-level advantage does not explicitly identify which elements differ in quality across sampled outputs or where their errors occur. We introduce Page-to-Token Policy Optimization (P2TPO), a structure-aligned credit-assignment framework that operates on full-page policy trajectories. Reference-element alignment first associates block-level quality contrasts with generated spans. Unified Anchor Pricing (UAP) incorporates matched and missing element outcomes and provides feedback relative to a fixed quality ceiling when sampled outputs receive identical but imperfect scores for a reference element. Within matched blocks, Span Credit Redistribution (SCR) redistributes existing block credit according to aligned correct and erroneous spans, using boundary proxies for within-block omissions while preserving the total block-derived advantage across its tokens. Under a fixed-weight additive page utility, we show that element-indexed contrasts retain local quality differences that page-level aggregation…

    AI50PDF
  • arXiv cs.CVComputer visionOct 15

    BridgeAfford: Bridging Query-Target Gap for Generalizable 3D Affordance Grounding

    Yijie Su, Yakun Zhu, Yi Bin, Pengpeng Zeng, Liang Chen +3

    Language-guided 3D affordance grounding aims to localize functional regions on 3D objects according to interaction intents. However, existing methods exhibit poor generalization to unseen objects. One challenge is the query-target gap: a semantic discrepancy between query features encoding interaction intents and target-region features, leading to inaccurate grounding. End-to-end training favors query representations tailored to seen objects, limiting their compatibility with shifted target-region features on unseen objects. To address this issue, we propose BridgeAfford, a framework that reduces the query-target semantic gap from two complementary perspectives. First, Interaction Region Alignment (IRA) promotes cross-object alignment among geometrically similar interaction regions during training. This enables structures on unseen objects that resemble previously observed ones to produce representations similar to those learned during training, facilitating target-region recognition. Second, Visual Evidence Aggregation (VEA) leverages a 2D affordance model to refine the attention distribution and guide the query toward the correct region, yielding more target-aware queries.…

    AI60PDF
  • arXiv cs.CVComputer visionOct 15

    SurgStreamer: proactive, request-driven monitoring of surgical video streams with structured state and a reusable reasoning layer

    Bowen Xiang, Jon S. Heiselman, Michael I. Miga

    Surgical teams could benefit from assistants that watch the video for requested events, such as "tell me when the clip applier appears during Calot triangle dissection", and answer questions about what has already happened. We formalise this task as request-driven event detection with exact semantics and causal alert decisions, and present SurgStreamer. It separates procedure-specific perception, which maps the 1-FPS stream to a structured state over tools, actions and phases, from procedure-agnostic reasoning, which evaluates a formal monitoring condition over this state and an event memory, either with exact rules or with a small controller that addresses entities through pointers rather than names. We evaluate this monitoring core with gold conditions. On a pre-registered, sealed test set of 12 laparoscopic cholecystectomies, SurgStreamer reached a macro event-F1 of 0.598 over full procedures. On identical 75-s windows with only the window available to every method, it scored 0.677. The tested configurations of the streaming vision-language model OneStreamer-4B, including ones fine-tuned on SurgStreamer's state or on video with an adapted vision encoder, scored at most 0.540…

    AIAgents70PDF
  • arXiv cs.CVComputer visionOct 15

    Temporal Periodic Image Registration with Implicit Neural Representations

    Mathias Lowes, Kristine Aavild Sørensen, Maxime Sermesant, Rasmus R. Paulsen

    Implicit Neural Representations (INRs) have recently gained popularity for their ability to model functions using simple and lightweight networks. These methods have also proven effective in deformable image registration. In this work, we extend INRs for deformable image registration to the domain of temporal image registration, focusing on periodic temporal image sequences. Our approach, Temporal-IDIR, optimizes a single INR to model deformations across all frames in a temporal image sequence simultaneously, allowing for self-regularization through its own deformation predictions. To achieve this, we introduce a temporal consistency loss that penalizes discrepancies between direct source-to-target transformations and those traversing intermediate frames. We evaluate our framework on the DIR-LAB dataset, using the target registration error (TRE) between annotated and moved landmarks as the metric. Here, we achieve a TRE of 1.03 mm, outperforming other INR-based registration methods. Additionally, our framework supports smooth interpolation between time frames by estimating deformations between the given input frames. Code is publicly available at…

    AI50PDF
  • arXiv cs.CVComputer visionOct 15

    Teaching DINOv3 About Partial 3D Geometry: A Self-Supervised Geometry-Aware Approach

    Viktoria Ehm, Dongliang Cao, Riccardo Marin, Daniel Scholz, Weikang Wang +2

    Partial shape matching is a crucial yet underexplored problem in 3D vision, with significant relevance to real-world scenarios where shapes are often only partially observed. Existing feature descriptors face difficulties in this setting, as traditional representations either struggle with the boundaries of partial shapes or heavily depend on the shape's spatial position. While existing approaches have employed DINO features for partial shape matching, these features are not inherently suited for handling partial observations. In this work, we propose a method to refine DINO features using LoRA-based self-supervised learning, enabling the generation of feature descriptors that are robust to partiality. Our features substantially improve performance on partial shape matching compared to traditional or vision foundation features. Additionally, when integrated into existing partial shape matching pipelines, we achieve state-of-the-art results on partial shape matching and left-right prediction benchmarks.

    AI60PDF
  • arXiv cs.CVComputer visionOct 15

    DINO-FIL: Multi-Layer Transformer Fusion for Generative Inpainting Forgery Localization

    Simon Freitter, David Fischinger, Silvia Poletti, Marcel Hasenbalg, Martin Boyer

    We propose DINO-FIL, a transformer-based framework for pixel-level localization of generative image forgeries. The method leverages multi-layer representations from a pretrained DINOv3 ViT-H+/16 encoder combined with a lightweight fusion decoder to capture both semantic structure and fine-grained texture inconsistencies introduced by image editing. By aggregating intermediate transformer features across depths, the model improves sensitivity to subtle, spatially localized artifacts that are often missed by standard single-layer approaches. We evaluate DINO-FIL in a cross-dataset setting, training on B-Free and testing on Beyond the Brush, CocoGlide, TGIF, and SAGI-D. The model achieves strong generalization, outperforming existing baselines on average and on four of five evaluated datasets, while maintaining a high true negative rate on pristine images.

    AI70PDF
  • arXiv cs.CVComputer visionOct 15

    FuseAdapt: Parameter-Efficient Multimodal Fusion for Semantic Segmentation with Missing Modalities

    Xin Zhang, Robby T. Tan

    Multi-modal semantic segmentation benefits from complementary sensors, but this benefit is fragile when some modalities are degraded or missing at test time. Many existing methods rely on dedicated fusion modules or multi-branch cross-modal encoders, and the fusion often becomes unreliable when modalities are absent, leading to substantial performance drops. We propose \textbf{FuseAdapt}, a parameter-efficient framework that performs fusion within the adaptation space of a frozen vision foundation model (VFM), exploring whether VFM priors can serve as an effective shared backbone for multi-modal segmentation. Instead of attaching a standalone fusion network, FuseAdapt assigns each modality a lightweight adaptation branch and performs \emph{adaptation-space fusion} by keeping Q/V low-rank adaptation private to each modality and routing only FFN low-rank residual activations across modalities inside the frozen backbone. The pretrained VFM remains fixed while modality-specific corrections and explicit cross-modal interaction are learned through the adaptation paths. To handle missing modalities, we further introduce \textbf{Predictive Modality Modeling (PMM)}, a direct…

    AI70PDF
  • arXiv cs.CVComputer visionOct 15

    Mixture of Multi-Sequence Feature Difference for Contrast-Enhanced MRI Synthesis

    Jonghun Kim, Junmo Kwon, Hyunjin Park

    Contrast-enhanced magnetic resonance imaging (CE-MRI) is crucial for accurately diagnosing and segmenting brain tumors. However, the acquisition of CE-MRI requires injecting contrast agents, which poses potential health risks and increases scan costs. Here, we propose a CE-MRI synthesis method from non-contrast sequences, reducing both risk and cost. Unlike existing methods that rely on channel-wise concatenation to fuse multi-sequence information for synthesis, we introduce a Mixture of Feature Difference (MoFD) module. The proposed module uses a dynamic weighting strategy to adaptively select and aggregate informative features from both individual source sequences and explicit differences among them. Quantitative and qualitative evaluations demonstrate that our proposed method outperforms baselines in terms of image fidelity and perceptual quality on the UCSF-PDGM and UPenn-GBM datasets. Our method successfully reconstructs tumor regions and achieves segmentation performance comparable to that obtained with real CE-MRI. Our ablation studies show that although adversarial learning improves perceptual quality, a regression-based variant of our method achieves higher structural…

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