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首次观测 · 记录 #1CVF · CVPR 2026:cvf:833d8569a54d10fdcecfd30adf6f41d8CVF · CVPR 2026

Expanding mmWave Datasets for Human Pose Estimation with Unlabeled Data and LiDAR Datasets

Peng, Zhuoxuan, Zhu, Boan, Zhang, Xingjian, Li, Wenying, Chan, S.-H. Gary

原文摘要

Current millimeter-wave (mmWave) datasets for human pose estimation (HPE) are scarce and lack diversity in both point cloud (PC) attributes and human poses, hindering the generalization ability of their trained models. On the other hand, unlabeled mmWave HPE data and diverse LiDAR HPE datasets are readily available. We propose EMDUL, a novel approach to expand the volume and diversity of an existing mmWave dataset using unlabeled mmWave data and LiDAR datasets. EMDUL consists of two independent modules, namely a pseudo-label estimator to annotate unlabeled mmWave data, and a closed-form converter that translates an annotated LiDAR PC to its mmWave counterpart. Expanding the original dataset with both LiDAR-converted and pseudo-labeled mmWave PCs significantly boosts the performance and generalization ability of all the examined HPE models, reducing 15.1% and 18.9% error for in-domain and out-of-domain settings, respectively. Code is available at https://github.com/Shimmer93/EMDUL.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:834505b1dcef9b7af5ff2528e3671df0CVF · CVPR 2026

Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning

Li, Yifei, Zheng, Wenzhao, Zhang, Yanran, Sun, Runze, Zheng, Yu, Chen, Lei, Zhou, Jie, Lu, Jiwen

原文摘要

The misuse of AI-driven video generation technologies has raised serious social concerns, highlighting the urgent need for reliable AI-generated video detectors. However, most existing methods are limited to binary classification and lack the necessary explanations for human interpretation. In this paper, we present Skyra, a specialized multimodal large language model (MLLM) that identifies human-perceivable visual artifacts in AI-generated videos and leverages them as grounded evidence for both detection and explanation. To support this objective, we construct ViF-CoT-4K for Supervised Fine-Tuning (SFT), which represents the first large-scale AI-generated video artifact dataset with fine-grained human annotations.We then develop a two-stage training strategy that systematically enhances our model's spatio-temporal artifact perception, explanation capability, and detection accuracy. To comprehensively evaluate Skyra, we introduce ViF-Bench, a benchmark comprising 3K high-quality samples generated by over ten state-of-the-art video generators. Extensive experiments demonstrate that Skyra surpasses existing methods across multiple benchmarks, while our evaluation yields valuable insights for advancing explainable AI-generated video detection. Our code, models, and datasets will be made publicly available.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8348fa25815f0c4069c4b35cc60b247fCVF · CVPR 2026

VISion On Request: Enhanced VLLM efficiency with sparse, dynamically selected, vision-language interactions

Bulat, Adrian, Baldrati, Alberto, Metaxas, Ioannis Maniadis, Ouali, Yassine, Tzimiropoulos, Georgios

原文摘要

Existing approaches for improving the efficiency of Large Vision-Language Models (LVLMs) are largely based on the concept of visual token reduction. This approach, however, creates an information bottleneck that impairs performance, especially on challenging tasks that require fine-grained understanding and reasoning. In this work, we challenge this paradigm by introducing VISion On Request (VISOR), a method that reduces inference cost without discarding visual information. Instead of compressing the image, VISOR improves efficiency by sparsifying the interaction between image and text tokens. Specifically, the language model attends to the full set of high-resolution visual tokens through a small, strategically placed set of attention layers: general visual context is provided by efficient cross-attention between text-image, while a few well-placed and dynamically selected self-attention layers refine the visual representations themselves, enabling complex, high-resolution reasoning when needed. Based on this principle, we first train a single universal network on a range of computational budgets by varying the number of self-attention layers, and then introduce a lightweight policy mechanism that dynamically allocates visual computation based on per-sample complexity. Extensive experiments show that VISOR drastically reduces computational cost while matching or exceeding state-of-the-art results across a diverse suite of benchmarks, and excels in challenging tasks that require detailed visual understanding.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:834c57116aa3b1a5659be7f2ba64e436CVF · CVPR 2026

Streaming Diffusion Model for Fast Infrared and Visible Video Fusion

Liu, Jinyuan, Sun, Ludan, Ma, Tengyu, Yang, Chunyan, Jiang, Zhiying, Ma, Long, Liu, Risheng, Fan, Xin

原文摘要

Infrared and visible video fusion is pivotal for robust perceptual systems, aiming to synthesize a comprehensive video stream that leverages both thermal resilience and textured details. However, prevailing methods, by treating videos as sequences of independent frames, inherently introduce temporal incoherence, such as flickering and ghosting artifacts. While diffusion models possess strong generative priors to remedy this, their iterative nature is prohibitively slow for video. To resolve this fundamental dilemma, we propose a streaming diffusion model for efficient infrared and visible video fusion, termed SDMFusion. Our key insight is to exploit the generative prior of a pre-trained diffusion model into a one-step sampling framework, while explicitly modeling temporal dynamics. We design a memory-augmented latent pipeline where a temporal aggregation adapter aligns and propagates cross-frame features to ensure coherence, supported by a dedicated temporal consistency loss. This approach effectively decouples the challenge of achieving high fidelity from maintaining temporal stability. Extensive experiments on four benchmarks demonstrate that our method establishes a new state-of-the-art, generating fused videos with exceptional spatio-temporal consistency at a speed suitable for real-time application. The code is available at https://github.com/DandanYoung/SDMFusion.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:83620e677fa553568219b18cd79da20dCVF · CVPR 2026

CogniVerse: Revolutionizing Multi-Modal Retrieval-Augmented Generation with Cognitive Reflection and Geometric Reasoning

Fang, Xiang, Fang, Wanlong, Wang, Changshuo

原文摘要

Multi-modal Retrieval-Augmented Generation (MMRAG) has emerged as a powerful paradigm for enhancing Multimodal Large Language Models (MLLMs) in knowledge-intensive question answering by integrating external visual, textual, and structural knowledge. However, existing MMRAG frameworks suffer from critical limitations, including noisy and irrelevant retrieval, cross-modal semantic misalignment, lack of adaptive reasoning, and incoherent generation across local and global contexts. We introduce CogniVerse, a novel MMRAG framework that addresses these challenges through a cognitive-inspired, mathematically rigorous approach. Drawing from human-like reasoning, CogniVerse integrates three synergistic components: (1) a Cognitive Reflection Module (CRM) that dynamically assesses retrieval necessity and filters relevant multi-modal content, reducing noise and computational overhead; (2) a Multi-modal Retrieval Module that aligns embeddings in a Riemannian manifold using information geometry and refines knowledge graphs via spectral graph theory, ensuring precise and coherent retrieval; and (3) a Hierarchical Generation Module that employs an optimal transport-based loss to balance token-level accuracy and global semantic coherence. Grounded in advanced theoretical frameworks, including convergence guarantees for geometric alignment and spectral optimization, CogniVerse achieves robust cross-modal integration and adaptive knowledge utilization. Extensive experiments demonstrate that CogniVerse significantly outperforms state-of-the-art systems in both accuracy and coherence, while reducing retrieval latency.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:83717d52a1c915f22da2986be42102fdCVF · CVPR 2026

Do Vision-Language Models Measure Up? Benchmarking Visual Measurement Reading with MeasureBench

Lin, Fenfen, Liu, Yesheng, Xu, Haiyu, Chen, Yue, He, Zheqi, Zhao, Mingxuan, Chen, Miguel Hu, Yao, Jin-Ge 等 9 位作者

原文摘要

Reading measurement instruments is effortless for humans and requires relatively little domain expertise, yet it remains surprisingly challenging for current vision-language models (VLMs) as we find in preliminary evaluation. In this work, we introduce MeasureBench, a benchmark on visual measurement reading covering both real-world and synthesized images of various types of measurements, along with an extensible pipeline for data synthesis. Our pipeline procedurally generates a specified type of gauge with controllable visual appearance, enabling scalable variation in key details such as pointers, scales, fonts, lighting, and clutter. Evaluation on popular proprietary and open-weight VLMs shows that even the strongest frontier VLMs struggle with measurement reading in general. We have also conducted preliminary experiments with reinforcement finetuning (RFT) over synthetic data, and find a significant improvement on both in-domain synthetic subset and real-world images. Our analysis highlights a fundamental limitation of current VLMs in fine-grained spatial grounding. We hope this resource and our code releases can help future advances on visually grounded numeracy and precise spatial perception of VLMs, bridging the gap between recognizing numbers and measuring the world.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8399109416421b82592cf10202401bdcCVF · CVPR 2026

TEAR: Temporal-aware Automated Red-teaming for Text-to-Video Models

He, Jiaming, Hou, Guanyu, Li, Hongwei, Huang, Zhicong, Chen, Kangjie, Yu, Yi, Jiang, Wenbo, Xu, Guowen 等 9 位作者

原文摘要

Text-to-Video (T2V) models are capable of synthesizing high-quality, temporally coherent dynamic video content, but the diverse generation also inherently introduces critical safety challenges. Existing safety evaluation methods, which focus on static image and text generation, are insufficient to capture the complex temporal dynamics in video generation. To address this, we propose a TEmporal-aware Automated Red-teaming framework, named TEAR, an automated framework designed to uncover safety risks specifically linked to the dynamic temporal sequencing of T2V models. TEAR employs a temporal-aware test generator optimized via a two-stage approach: initial generator training and temporal-aware online preference learning, to craft textually innocuous prompts that exploit temporal dynamics to elicit policy-violating video output. And a refine model is adopted to improve the prompt stealthiness and adversarial effectiveness cyclically. Extensive experimental evaluation demonstrates the effectiveness of TEAR across open-source and commercial T2V systems with an over 80% attack success rate, a significant boost from the prior best result of 57%.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:839fad144b6b0a6903d7e34d0874c2b4CVF · CVPR 2026

Self-Evaluation Unlocks Any-Step Text-to-Image Generation

Yu, Xin, Qi, Xiaojuan, Li, Zhengqi, Zhang, Kai, Zhang, Richard, Lin, Zhe, Shechtman, Eli, Wang, Tianyu 等 9 位作者

原文摘要

We introduce the Self-Evaluating Model (Self-E), a novel, from-scratch training approach for text-to-image generation that supports any-step inference. Self-E learns from data similarly to a Flow Matching model, while simultaneously employing a novel self-evaluation mechanism: it evaluates its own generated samples using its current score estimates, effectively serving as a dynamic self-teacher. Unlike traditional diffusion or flow models, it does not rely solely on local supervision, which typically necessitates many inference steps. Unlike distillation-based approaches, it does not require a pretrained teacher. This combination of instantaneous local learning and self-driven global matching bridges the gap between the two paradigms, enabling the training of a high-quality text-to-image model from scratch that excels even at very low step counts. Extensive experiments on large-scale text-to-image benchmarks show that Self-E not only excels in few-step generation, but is also competitive with state-of-the-art Flow Matching models at 50 steps. We further find that its performance improves monotonically as inference steps increase, enabling both ultra-fast few-step generation and high-quality long-trajectory sampling within a single unified model. To our knowledge, Self-E is the first from-scratch, any-step text-to-image model, offering a unified framework for efficient and scalable generation.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:83a745708a4987a5c286b3cf0fc7ed4fCVF · CVPR 2026

CoSMo3D: Open-World Promptable 3D Semantic Segmentation through LLM-Guided Canonical Spatial Modeling

Jin, Li, Chen, Weikai, Wang, Yujie, Yin, Yingda, Hu, Zeyu, Zhang, Runze, Luo, Keyang, Qian, Shengju 等 10 位作者

原文摘要

Open-world promptable 3D semantic segmentation remains brittle as semantics are inferred in the input sensor coordinates. Yet, humans, in contrast, interpret parts via functional roles in a canonical space -- wings extend laterally, handles protrude to the side, and legs support from below. Psychophysical evidence shows that we mentally rotate objects into canonical frames to reveal these roles. To fill this gap, we propose CoSMo3D, which attains canonical space perception by inducing a latent canonical reference frame learned directly from data. By construction, we create a unified canonical dataset through LLM-guided intra- and cross-category alignment, exposing canonical spatial regularities across 200 categories. By induction, we realize canonicality inside the model through a dual-branch architecture with canonical map anchoring and canonical box calibration, collapsing pose variation and symmetry into a stable canonical embedding. This shift from input pose space to canonical representation yields far more stable and transferable part semantics. Experimental results show that CoSMo3D establishes new state of the art in open-world promptable 3D segmentation.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:83c1c4275485e1bb543ecabc641f7ce3CVF · CVPR 2026

From Scale to Speed: Adaptive Test-Time Scaling for Image Editing

Qu, Xiangyan, Yuan, Zhenlong, Tang, Jing, Chen, Rui, Tang, Datao, Yu, Meng, Sun, Lei, Bai, Yancheng 等 12 位作者

原文摘要

Image Chain-of-Thought (Image-CoT) is a test-time scaling paradigm that improves image generation by extending inference time. Most Image-CoT methods focus on text-to-image (T2I) generation. Unlike T2I generation, image editing is goal-directed: the solution space is constrained by the source image and instruction. This mismatch causes three challenges when applying Image-CoT to editing: inefficient resource allocation with fixed sampling budgets, unreliable early-stage verification using general MLLM scores, and redundant edited results from large-scale sampling. To address this, we propose ADaptive Edit-CoT (ADE-CoT), an on-demand test-time scaling framework to enhance editing efficiency and performance. It incorporates three key strategies: (1) a difficulty-aware resource allocation that assigns dynamic budgets based on estimated edit difficulty; (2) edit-specific verification in early pruning that uses region localization and caption consistency to select promising candidates; and (3) depth-first opportunistic stopping, guided by an instance-specific verifier, that terminates when intent-aligned results are found. Extensive experiments on three SOTA editing models (Step1X-Edit, BAGEL, FLUX.1 Kontext) across three benchmarks show that ADE-CoT achieves superior performance-efficiency trade-offs. With comparable sampling budgets, ADE-CoT obtains better performance with more than 2x speedup over Best-of-N.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:83ee0a4744c3b006d64e0f6d42cedb11CVF · CVPR 2026

Seeing Through Touch: Tactile-Driven Visual Localization of Material Regions

Kim, Seongyu, Lee, Seungwoo, Ryu, Hyeonggon, Chung, Joon Son, Senocak, Arda

原文摘要

We address the problem of tactile localization, where the goal is to identify image regions that share the same material properties as a tactile input. Existing visuo-tactile methods rely on global alignment and thus fail to capture the fine-grained local correspondences required for this task. The challenge is amplified by existing datasets, which predominantly contain close-up, low-diversity images. We propose a model that learns local visuo-tactile alignment via dense cross-modal feature interactions, producing tactile saliency maps for touch-conditioned material segmentation. To overcome dataset constraints, we introduce: (i) in-the-wild multi-material scene images that expand visual diversity, and (ii) a material-diversity pairing strategy that aligns each tactile sample with visually varied yet tactilely consistent images, improving contextual localization and robustness to weak signals. We also construct two new tactile-grounded material segmentation datasets for quantitative evaluation. Experiments on both new and existing benchmarks show that our approach substantially outperforms prior visuo-tactile methods in tactile localization.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:83f464f26ea6c0c49c292d65d21a27c2CVF · CVPR 2026

C-LaV: Conditional Latent Velocity Field Denoising for Weather-Robust LiDAR Place Recognition

Cao, Xuewei, Yang, Jiayue, Zeng, Zhiwen, Zhang, Yanyong, Xia, Yan

原文摘要

LiDAR-based place recognition is highly sensitive to rain, snow, and fog, where scattering and attenuation distort geometric structure and intensity. We tackle this problem with Conditional Latent Velocity Field (C-LaV) denoising, which restores weather-robust representations before retrieval. Single-sweep point clouds are projected into three-channel bird's-eye-view (BEV) images and encoded with a frozen DINOv2-based BEV transformer to obtain a semantically anchored latent space shared across weather conditions. On this manifold, a conditional Flow Matching model learns a velocity field whose probability-flow ordinary differential equation (ODE) deterministically transports noisy latents toward their clear-weather counterparts. From the denoised manifold, a Sinkhorn Aggregation of Local Descriptors (SALAD) head produces compact global descriptors optimized with a truncated Smooth-AP loss. We also establish a unified adverse-weather benchmark with 3 m frame spacing and shared evaluation thresholds across KITTI, NCLT, and Boreas datasets. Under this protocol, C-LaV improves Recall@1 by 17.5% on NCLT snow and 21.5% on Boreas, achieving state-of-the-art weather robustness. Our dataset and code will be publicly available.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:841221dcd4420e79eb4a06d3f138c14cCVF · CVPR 2026

SuP: Sub-cloud Driven Point Cloud Registration

Fung, Sheldon, Pan, Wei, Cao, Ling, Hou, Fei, Chen, Ling, Mao, Shasha, Li, Hongdong, Lu, Xuequan

原文摘要

While existing point-cloud-registration methods can well handle high-overlap scenarios of two point clouds, they often struggle with low-overlap scenarios, due to inevitable geometric/semantic ambiguities in the non-overlapping regions. In this paper, we introduce SuP, a novel framework that reformulates low-overlap registration as a high-overlap sub-cloud pairs (anchor pairs) mining problem. Central to SuP is our Dual-phase Sub-cloud Anchor Mining (DSAM) module, which first subdivides the source and target point clouds into multiple sub-clouds, followed by introducing a dual-phase weighting pipeline: 1) an efficient overlap-guided prior-weighting scheme (OPS) that leverages feature salience to identify candidate anchor pairs, and 2) a multi-scale post-weighting network (MPN) that exploits neighborhood feature consensus to further identify anchor pairs. Subsequently, final correspondences are generated through a merge-to-match module using the anchor pairs. To train DSAM, we design an alignment-aware weighting loss that uses on-the-fly alignment errors as supervision. Comprehensive experiments on the color-enhanced 3DMatch and 3DLoMatch demonstrate that SuP significantly outperforms state-of-the-art methods, achieving higher registration recall and more accurate alignment, especially under challenging low-overlap conditions.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:842cbd7864a0f780d82cc215887dccafCVF · CVPR 2026

One-to-More: High-Fidelity Training-Free Anomaly Generation with Attention Control

Rao, Haoxiang, Wang, Zhao, Si, Chenyang, Lyu, Yan, Duan, Yuanyi, Zhao, Fang, Shan, Caifeng

原文摘要

Industrial anomaly detection (AD) is characterized by an abundance of normal images but a scarcity of anomalous ones. Although numerous few-shot anomaly synthesis methods have been proposed to augment anomalous data for downstream AD tasks, most existing approaches require time-consuming training and struggle to learn distributions that are faithful to real anomalies, thereby restricting the efficacy of AD models trained on such data. To address these limitations, we propose a training-free few-shot anomaly generation method, namely O2MAG, which leverages the self-attention in One reference anomalous image to synthesize More realistic anomalies, supporting effective downstream anomaly detection. Specifically, O2MAG manipulates three parallel diffusion processes via self-attention grafting and incorporates the anomaly mask to mitigate foreground-background query confusion, synthesizing text-guided anomalies that closely adhere to real anomalous distributions. To bridge the semantic gap between the encoded anomaly text prompts and the true anomaly semantics, Anomaly-Guided Optimization is further introduced to align the synthesis process with the target anomalous distribution, steering the generation toward realistic and text-consistent anomalies. Moreover, to mitigate faint anomaly synthesis inside anomaly masks, Dual-Attention Enhancement is adopted during generation to reinforce both self- and cross-attention on masked regions. Extensive experiments validate the effectiveness of \method, demonstrating its superior performance over prior state-of-the-art methods on downstream AD tasks.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:8435411d093da6addc68228e5ad86e2fCVF · CVPR 2026

AHS: Adaptive Head Synthesis via Synthetic Data Augmentations

Kang, Taewoong, Jang, Hyojin, Jeong, Sohyun, Moon, Seunggi, Kim, Gihwi, Jung, Hoon Jin, Choo, Jaegul

原文摘要

Recent digital media advancements have created increasing demands for sophisticated portrait manipulation techniques, particularly head swapping--where one image's head is seamlessly integrated onto another's body. Current approaches predominantly rely on face-centered cropped data with limited view angles, significantly restricting their real-world applicability. These methods struggle with diverse head expressions, varying hairstyles, and natural blending beyond facial regions. To address these limitations, we propose Adaptive Head Synthesis (AHS), which effectively handles full upper-body images with varied head poses and expressions. AHS incorporates a novel head reenacted synthetic data augmentation strategy to overcome self-supervised training constraints, enhancing generalization across diverse facial expressions and orientations without requiring paired training data. Comprehensive experiments demonstrate that our approach achieves superior performance in challenging real-world scenarios, producing visually coherent results that preserve both identity and expression fidelity across various head orientations and hairstyles. Notably, our method shows exceptional robustness in maintaining facial identity while drastic expression changes and faithfully preserving accessories while significant head pose variations.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8448b1620bcc39d88e2c9d34bfe52a78CVF · CVPR 2026

Hierarchical Long Video Understanding with Audiovisual Entity Cohesion and Agentic Search

Yin, Xinlei, Peng, Xiulian, Li, Xiao, Xiong, Zhiwei, Lu, Yan

原文摘要

Long video understanding presents significant challenges for vision-language models due to extremely long context windows. Existing solutions relying on naive chunking strategies with retrieval-augmented generation, typically suffer from information fragmentation and a loss of global coherence. We present HAVEN, a unified framework for long-video understanding that enables coherent and comprehensive reasoning by integrating audiovisual entity cohesion and hierarchical video indexing with agentic search. First, we preserve semantic consistency by integrating entity-level representations across visual and auditory streams, while organizing content into a structured hierarchy spanning global summary, scene, segment, and entity levels. Then we employ an agentic search mechanism to enable dynamic retrieval and reasoning across these layers, facilitating coherent narrative reconstruction and fine-grained entity tracking. Extensive experiments demonstrate that our method achieves good temporal coherence, entity consistency, and retrieval efficiency, establishing a new state-of-the-art with an overall accuracy of 84.1% on LVBench. Notably, it achieves outstanding performance in the challenging reasoning category, reaching 80.1%. These results highlight the effectiveness of structured, multimodal reasoning for comprehensive and context-consistent understanding of long-form videos.

cs.CV · cs.CL · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:8450a8aa0c7dcd7010821bbde2fb8c89CVF · CVPR 2026

V^2-SAM: Marrying SAM2 with Multi-Prompt Experts for Cross-View Object Correspondence

Pan, Jiancheng, Wang, Runze, Qian, Tianwen, Mahdi, Mohammad, Fu, Yanwei, Xue, Xiangyang, Huang, Xiaomeng, Van Gool, Luc 等 10 位作者

原文摘要

Cross-view object correspondence, exemplified by the representative task of ego-exo object correspondence, aims to establish consistent associations of the same object across different viewpoints (e.g., ego-centric and exo-centric). This task poses significant challenges due to drastic viewpoint and appearance variations, making existing segmentation models, such as SAM2, non-trivial to apply directly. To address this, we present V^ 2 -SAM, a unified cross-view object correspondence framework that adapts SAM2 from single-view segmentation to cross-view correspondence through two complementary prompt generators. Specifically, the Cross-View Anchor Prompt Generator (V^ 2 -Anchor), built upon DINOv3 features, establishes geometry-aware correspondences and, for the first time, unlocks coordinate-based prompting for SAM2 in cross-view scenarios, while the Cross-View Visual Prompt Generator (V^ 2 -Visual) enhances appearance-guided cues via a novel visual prompt matcher that aligns ego-exo representations from both feature and structural perspectives. To effectively exploit the strengths of both prompts, we further adopt a multi-expert design and introduce a Post-hoc Cyclic Consistency Selector (PCCS) that adaptively selects the most reliable expert based on cyclic consistency. Extensive experiments validate the effectiveness of V^ 2 -SAM, achieving new state-of-the-art performance on Ego-Exo4D (Ego-Exo object correspondence), DAVIS-2017 (video object tracking), and HANDAL-X (robotic-ready cross-view correspondence).

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:846826936bbcf01d675b524dfc420496CVF · CVPR 2026

RegionRoute: Regional Style Transfer with Diffusion Model

Chen, Bowen, Zuena, Jake, Bovik, Alan C., Kothandaraman, Divya

原文摘要

Precise spatial control in diffusion-based style transfer remains challenging. This challenge arises because diffusion models treat style as a global feature and lack explicit spatial grounding of style representations, making it difficult to restrict style application to specific objects or regions. To our knowledge, existing diffusion models are unable to perform true localized style transfer, typically relying on handcrafted masks or multi-stage post-processing that introduce boundary artifacts and limit generalization. To address this, we propose an attention-supervised diffusion framework that explicitly teaches the model where to apply a given style by aligning the attention scores of style tokens with object masks during training. Two complementary objectives, a Focus loss based on KL divergence and a Cover loss using binary cross-entropy, jointly encourage accurate localization and dense coverage. A modular LoRA-MoE design further enables efficient and scalable multi-style adaptation. To evaluate localized stylization, we introduce the Regional Style Editing Score, which measures Regional Style Matching through CLIP-based similarity within the target region and Identity Preservation via masked LPIPS and pixel-level consistency on unedited areas. Experiments show that our method achieves mask-free, single-object style transfer at inference, producing regionally accurate and visually coherent results that outperform existing diffusion-based editing approaches.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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DepthFocus: Controllable Depth Estimation for See-Through Scenes

Min, Junhong, Kim, Jimin, Kim, Minwook, Min, Cheol-Hui, Jeon, Youngpil, Choi, Minyong

原文摘要

Depth in the real world is rarely singular. Transmissive materials create layered ambiguities that confound conventional perception systems. Existing models remain passive; conventional approaches typically estimate static depth maps anchored to the nearest surface, and even recent multi-head extensions suffer from a representational bottleneck due to fixed feature representations. This stands in contrast to human vision, which actively shifts focus to perceive a desired depth. We introduce DepthFocus, a steerable Vision Transformer that redefines stereo depth estimation as condition-aware control. Instead of extracting fixed features, our model dynamically modulates its computation based on a physical reference depth, integrating dual conditional mechanisms to selectively perceive geometry aligned with the desired focus. Leveraging a newly curated large-scale synthetic dataset, DepthFocus achieves state-of-the-art results across all evaluated benchmarks, including both standard single-layer and complex multi-layered scenarios. While maintaining high precision in opaque regions, our approach effectively resolves depth ambiguities in transparent and reflective scenes by selectively reconstructing geometry at a target distance. This capability enables robust, intent-driven perception that significantly outperforms existing multi-layer methods, marking a substantial step toward active 3D perception. \noindent Project page: \href https://junhong-3dv.github.io/depthfocus-project/ this https URL .

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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MuViT: Multi-Resolution Vision Transformers for Learning Across Scales in Microscopy

Mantes, Albert Dominguez, La Manno, Gioele, Weigert, Martin

原文摘要

Modern microscopy routinely produces gigapixel images that contain structures across multiple spatial scales, from fine cellular morphology to broader tissue organization. Many analysis tasks require combining these scales, yet most vision models operate at a single resolution or derive multi-scale features from one view, limiting their ability to exploit the inherently multi-resolution nature of microscopy data. We introduce MuViT, a transformer architecture built to fuse true multi-resolution observations from the same underlying image. MuViT embeds all patches into a shared world-coordinate system and extends rotary positional embeddings to these coordinates, enabling attention to integrate large-scale context with high-resolution detail within a single encoder. Across synthetic benchmarks, kidney histopathology, and high-resolution mouse-brain microscopy, MuViT delivers consistent improvements over strong ViT and CNN baselines. Multi-resolution MAE pretraining further produces scale-consistent representations that enhance downstream tasks. These results demonstrate that explicit world-coordinate modelling provides a simple yet powerful mechanism for leveraging multi-resolution information in large-scale microscopy analysis. Code is available at github.com/weigertlab/muvit.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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What Do Visual Tokens Really Encode? Uncovering Sparsity and Redundancy in Multimodal Large Language Models

Fan, Yingqi, Tong, Junlong, Zhao, Anhao, Shen, Xiaoyu

原文摘要

Multimodal large language models (MLLMs) project visual tokens into the embedding space of language models, yet the internal structuring and processing of visual semantics remain poorly understood. In this work, we introduce a two-fold analytical framework featuring a novel probing tool, EmbedLens, to conduct a fine-grained analysis. We uncover a pronounced semantic sparsity at the input level: visual tokens consistently partition into sink, dead, and alive categories. Remarkably, only the alive tokens, comprising ~60% of the total input, carry image-specific meaning. Furthermore, using a targeted patch-compression benchmark, we demonstrate that these alive tokens already encode rich, fine-grained cues (e.g., objects, colors, and OCR) prior to entering the LLM. Internal visual computations (such as visual attention and feed-forward networks) are redundant for most standard tasks. For the small subset of highly vision-centric tasks that actually benefit from internal processing, we reveal that alive tokens naturally align with intermediate LLM layers rather than the initial embedding space, indicating that shallow-layer processing is unnecessary and that direct mid-layer injection is both sufficient. Ultimately, our findings provide a unified mechanistic view of visual token processing, paving the way for more efficient and interpretable MLLM architectures through selective token pruning, minimized visual computation, and mid-layer injection.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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GPFlow: Gaussian Prototype Probability Flow for Unsupervised Multi-Modal Anomaly Detection

Li, Yiting, Yang, Xulei, Liao, Jingyi, Zhang, Jing, Liu, Fayao

原文摘要

In this paper, we study unsupervised multi-modal anomaly detection under challenging few-shot conditions, where only a few normal training samples are available for each class. To prevent the trivial reconstruction of anomalies, recent methods often rely on discrete prototypes to establish an information bottleneck. However, such discrete prototypes fail to capture continuous and anisotropic variations of normal features. We therefore propose GPFlow, a probability-flow-inspired framework that models normality using learnable Gaussian prototypes. The key component of GPFlow is an analytical Posterior-Mean Path (PMP) router, which reconstructs features through the posterior mean of a noise-smoothed Gaussian mixture. This yields anisotropic shrinkage as a covariance-aware information bottleneck: PMP selectively preserves normal variations aligned with the covariance structure of Gaussian prototypes while strictly suppressing deviations inconsistent with the prototypes. To exploit complementary knowledge across modalities, GPFlow further combines intra-modal and cross-modal reconstruction, and applies a lightweight instance-aware prior calibration to alleviate the distribution mismatch between sparse training data and diverse test samples. Experiments on MVTec-3D-AD and Eyecandies show that GPFlow achieves significant performance improvement with only a few normal training samples while remaining computationally efficient.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Space-Time Forecasting of Dynamic Scenes with Motion-aware Gaussian Grouping

Lee, Junmyeong, Choi, Hoseung, Cho, Minsu

原文摘要

Forecasting dynamic scenes remains a fundamental challenge in computer vision, as limited observations make it difficult to capture coherent object-level motion and long-term temporal evolution.We present Motion Group-aware Gaussian Forecasting (MoGaF), a framework for long-term scene extrapolation built upon the 4D Gaussian Splatting representation.MoGaF introduces motion-aware Gaussian grouping and group-wise optimization to enforce physically consistent motion across both rigid and non-rigid regions, yielding spatially coherent dynamic representations.Leveraging this structured space-time representation, a lightweight forecasting module predicts future motion, enabling realistic and temporally stable scene evolution.Experiments on synthetic and real-world datasets demonstrate that MoGaF consistently outperforms existing baselines in rendering quality, motion plausibility, and long-term forecasting stability.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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SegEarth-R2: Towards Comprehensive Language-guided Segmentation for Remote Sensing Images

Xin, Zepeng, Li, Kaiyu, Chen, Luodi, Li, Wanchen, Yuchen, Xiao, Qiao, Hui, Zhang, Weizhan, Meng, Deyu 等 9 位作者

原文摘要

Effectively grounding complex language to pixels in remote sensing (RS) images is a critical challenge for applications like disaster response and environmental monitoring. Current models can parse simple, single-target commands but fail when presented with complex geospatial scenarios, e.g., segmenting objects at various granularities, executing multi-target instructions, and interpreting implicit user intent. To drive progress against these failures, we present LaSeRS, the first large-scale dataset built for comprehensive training and evaluation across four critical dimensions of language-guided segmentation: hierarchical granularity, target multiplicity, reasoning requirements, and linguistic variability. By capturing these dimensions, LaSeRS moves beyond simple commands, providing a benchmark for complex geospatial reasoning. This addresses a critical gap: existing datasets oversimplify, leading to sensitivity-prone real-world models. We also propose SegEarth-R2, an MLLM architecture designed for comprehensive language-guided segmentation in RS, which directly confronts these challenges. The model's effectiveness stems from two key improvements: (1) a spatial attention supervision mechanism specifically handles the localization of small objects and their components, and (2) a flexible and efficient segmentation query mechanism that handles both single-target and multi-target scenarios. Experimental results demonstrate that our SegEarth-R2 achieves outstanding performance on LaSeRS and other benchmarks, establishing a powerful baseline for the next generation of geospatial segmentation. All data and code will be released.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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StreamRAG: Enhancing Real-Time Video Understanding with Retrieval Augmentation

Xie, Junlin, Zheng, Quanlong, Zhang, Ruifei, Wang, Kuo, Zhang, Yanhao, Luo, Jinguo, Lu, Haonan, Wan, Xiang 等 9 位作者

原文摘要

The transition of Retrieval-Augmented Generation (RAG) from offline video analysis to online, streaming scenarios presents a set of critical, unexplored challenges. These include the need for on-the-fly semantic segmentation of continuous video, the inherent tension between low-latency processing and high-quality knowledge extraction, and the demand for query-specific temporal reasoning. We propose StreamRAG, a novel framework designed to overcome these hurdles. StreamRAG is built upon three core technical pillars: (1) a Stream Event Segmentation (SES) module that performs real-time boundary detection to chunk the stream into meaningful units; (2) a Token-Reusing Accelerator that drastically cuts down captioning latency by leveraging computational overlap between consecutive frames; and (3) a Dynamic Retrieval Gate that modulates the retrieval scope and strategy based on the query's temporal sensitivity and contextual similarity. Empirical evaluation confirms that StreamRAG establishes a new state-of-the-art, delivering superior accuracy with minimal latency in streaming video comprehension.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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First Logit Boosting: Visual Grounding Method to Mitigate Object Hallucination in Large Vision-Language Models

Ha, Jiwoo, Baek, Jongwoo, So, Jinhyun

原文摘要

Recent Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across various multimodal tasks that require understanding both visual and linguistic inputs. However, object hallucination -- the generation of nonexistent objects in answers -- remains a persistent challenge. Although several approaches such as retraining and external grounding methods have been proposed to mitigate this issue, they still suffer from high data costs or structural complexity. Training-free methods such as Contrastive Decoding (CD) are more cost-effective, avoiding additional training or external models, but still suffer from long-term decay, where visual grounding weakens and language priors dominate as the generation progresses. In this paper, we propose First Logit Boosting (FLB), a simple yet effective training-free technique designed to alleviate long-term decay in LVLMs. FLB stores the logit of the first generated token and adds it to subsequent token predictions, effectively mitigating long-term decay of visual information. We observe that FLB (1) sustains the visual information embedded in the first token throughout generation, and (2) suppresses hallucinated words through the stabilizing effect of the "The" token. Experimental results show that FLB significantly reduces object hallucination across various tasks, benchmarks, and backbone models. Notably, it causes negligible inference overhead, making it highly applicable to real-time multimodal systems. Code is available at https://github. com/jiwooha20/FLB

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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FedCART: Tackling Long-Tailed Distributions in Federated Adversarial Training via Classifier Refinement

Qin, Yuchen, Zhou, Yizhi, Wang, Junxiao, Xie, Xin, Qi, Heng

原文摘要

Growing privacy and security demands in the real world have spurred interest in adversarially robust Federated Learning (FL). While Adversarial Training (AT) is a well-established defense in centralized learning, its extension to the federated setting, known as Federated Adversarial Training (FAT), faces significant challenges due to data heterogeneity across clients. Existing FAT methods have made significant contributions, but they typically assume a balanced global data distribution, an assumption that rarely holds true in practice due to the prevalence of long-tailed distributions. This work first identifies and diagnoses the severe performance degradation of FAT under long-tailed data, attributing it to skewed feature representations and impaired classifier discriminability. To address this, we propose FedCART, a novel FAT framework that decouples the model into a shared feature extractor and a dual-classifier structure. On the client side, a representation-alignment loss enhances adversarial robustness, while gradient-based class prototypes are extracted for classifier calibration. On the server side, models and prototype sets are aggregated to synthesize balanced virtual features, enabling the re-training of an auxiliary classifier to mitigate long-tailed bias. Extensive experiments demonstrate that FedCART significantly improves both accuracy and robustness, outperforming state-of-the-art FAT methods. To the best of our knowledge, this is the first work to systematically investigate and address FAT under long-tailed distributions, representing a significant step toward practical adversarial robustness in FL.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Anchoring the Mind of Multimodal Reasoners: Cognitive Bias as a Vector for Jailbreak Attacks

Cong, Linhua, Sima, Bingrui, He, Kun

原文摘要

Multimodal Large Reasoning Models (MLRMs) exhibit remarkable performance on complex tasks by incorporating explicit multi-step reasoning. However, this capability also introduces new security vulnerabilities. Existing jailbreak studies largely overlook Cognitive-level weaknesses embedded in the reasoning process itself. In this work, we uncover a critical cognitive bias in MLRMs: the anchoring effect, where safety judgments are disproportionately influenced by the first piece of information received--the anchor. Building on this finding, we propose the Reasoning-chain Anchoring Attack (RA-Attack), a novel jailbreak framework that fully exploits this vulnerability. RA-Attack employs a cross-modal safe anchor, whose core component is a structured visual mind map. This structured format provides the model with a pre-established, safety-biased reasoning chain that subtly induces it to rationalize and execute subsequent harmful intent. Extensive experiments across seven leading closed- and open-source MLRMs demonstrate the effectiveness of RA-Attack, achieving state-of-the-art jailbreak success rates--92% on Gemini-2.5-Pro and 82% on GPT-4o. Our findings reveal that cognitive biases can be systematically exploited to manipulate multimodal reasoning chains, establishing cognitive security as a critical and underexplored frontier in AI safety research. Warning: This paper contains unsafe examples.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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MMTIT-Bench: A Multilingual and Multi-Scenario Benchmark with Cognition-Perception-Reasoning Guided Text-Image Machine Translation

Li, Gengluo, Zhang, Chengquan, Liang, Yupu, Shen, Huawen, Zhang, Yaping, Lyu, Pengyuan, Wang, Weinong, Wan, Xingyu 等 12 位作者

原文摘要

End-to-end text-image machine translation (TIMT), which directly translates textual content in images across languages, is crucial for real-world multilingual scene understanding. Despite advances in vision-language large models (VLLMs), robustness across diverse visual scenes and low-resource languages remains underexplored due to limited evaluation resources. We present MMTIT-Bench, a human-verified multilingual and multi-scenario benchmark with 1,400 images spanning fourteen non-English and non-Chinese languages and diverse settings such as documents, scenes, and web images, enabling rigorous assessment of end-to-end TIMT. Beyond benchmarking, we study how reasoning-oriented data design improves translation. Although recent VLLMs have begun to incorporate long Chain-of-Thought (CoT) reasoning, effective thinking paradigms for TIMT are still immature: existing designs either cascade parsing and translation in a sequential manner or focus on language-only reasoning, overlooking the visual cognition central to VLLMs. We propose Cognition-Perception-Reasoning for Translation (CPR-Trans), a data paradigm that integrates scene cognition, text perception, and translation reasoning within a unified reasoning process. Using a VLLM-driven data generation pipeline, CPR-Trans provides structured, interpretable supervision that aligns perception with reasoning. Experiments on 3B and 7B models show consistent gains in accuracy and interpretability. We will release MMTIT-Bench to promote the multilingual and multi-scenario TIMT research upon acceptance.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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Polarization State Tracing for Reflection Removal and Color-Consistent Reconstruction

Wang, Dongyue, Lu, Yang, Tian, Jiandong

原文摘要

Colored glass is widely used in everyday settings, yet its reflective and absorptive properties often introduce ghost shadows and color bias in captured images. However, existing methods typically neglect the absorption issue, making it difficult to address color bias caused by colored glass. To address this, we are the first to apply polarization imaging theory to model the light transmission process within glass. Specifically, we propose a novel imaging model, the Polarization State Tracing Model (PSTM), which traces polarized light along multiple propagation paths and accounts for wavelength-selective absorption, enabling joint reflection removal and color-consistent reconstruction. Guided by PSTM, we design a Channel Ring Attention (CRA) mechanism to efficiently capture inter-angle polarization dependencies and enhance feature interaction across polarization channels, ensuring physically consistent recovery. Besides, the recovered polarization information can be directly applied to advanced downstream tasks, such as Shape-from-Polarization (SfP). We construct a real-world dataset, GlassPol, containing a wide range of glass materials, enabling testing under diverse optical conditions. Extensive experiments show that our method outperforms existing state-of-the-art methods, achieving up to a 3dB improvement in PSNR, establishing a new benchmark for polarized reflection removal.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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