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首次观测 · 记录 #1CVF · CVPR 2026:cvf:044ed0f76303b6851f4fdbee9c0e7041CVF · CVPR 2026

Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification

Liu, Han, Georgescu, Bogdan, Zhang, Yanbo, Yoo, Youngjin, Baumgartner, Michael, Gao, Riqiang, Wang, Jianing, Zhao, Gengyan 等 11 位作者

原文摘要

3D medical image classification is essential to modern clinical workflows. Medical foundation models (FMs) have emerged as a promising approach for scaling to new tasks, yet current research suffers from three critical pitfalls: data-regime bias, suboptimal adaptation, and insufficient task coverage. In this paper, we address these pitfalls and introduce AnyMC3D, a scalable 3D classifier adapted from 2D FMs. It allows efficient scaling to new tasks by adding only lightweight plugins ( 1M parameters per task) to a single frozen backbone. Besides, this versatile framework also supports multi-view inputs, auxiliary pixel-level supervision, and interpretable heatmap generation. We establish a comprehensive benchmark of 12 tasks covering diverse pathologies, anatomies, and modalities and systematically evaluate state-of-the-art 3D classification techniques. Our analysis reveals several key insights: (1) effective adaptation is critical to unlock FM potential, (2) general-purpose FMs can match medical-specific FMs if properly adapted, and (3) 2D-based methods surpass 3D architectures for 3D classification. For the first time, we demonstrate the feasibility of achieving state-of-the-art performance across diverse applications using a single scalable framework (e.g., 1st place in the *** challenge), eliminating the need for separate task-specific 3D models.

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:0609ebbcfbb5bcf90d18daf5caaf41eeCVF · CVPR 2026

UniGenDet: A Unified Generative-Discriminative Framework for Co-Evolutionary Image Generation and Generated Image Detection

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

原文摘要

In recent years, significant progress has been made in both image generation and generated image detection. Despite their rapid, yet largely independent, development, these two fields have evolved distinct architectural paradigms: the former predominantly relies on generative networks, while the latter favors discriminative frameworks. A recent trend in both domains is the use of adversarial information to enhance performance, revealing potential for synergy. However, the significant architectural divergence between them presents considerable challenges. Departing from previous approaches, we propose **UniGenDet**: a **Uni**fied generative-discriminative framework for co-evolutionary image **Gen**eration and generated image **Det**ection. To bridge the task gap, we design a symbiotic multimodal self-attention mechanism and a unified fine-tuning algorithm. This synergy allows the generation task to improve the interpretability of authenticity identification, while authenticity criteria guide the creation of higher-fidelity images. Furthermore, we introduce a detector-informed generative alignment mechanism to facilitate seamless information exchange. Extensive experiments on multiple datasets demonstrate that our method achieves state-of-the-art performance. Code: https://github.com/Zhangyr2022/UniGenDet.

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:09ea3441aa692f9f96c7977963bec522CVF · CVPR 2026

Unified Latent Space for Understanding and Generation via Semantic Auto-encoder

Li, Xiaojie, Zhao, Yang, Li, Ming, Zhang, Yancheng, Lyu, Zonglin, Chen, Yunpeng, Wang, Rui, Zhou, Daquan

原文摘要

Latent generative modeling has emerged as the dominant paradigm for Diffusion Transformers (DiT), where a pretrained autoencoder compresses image pixels into a latent space to facilitate the diffusion process. Recently, the use of semantic encoders within autoencoders (AEs) has gained attention, yet their influence on image reconstruction and diffusion model training remains insufficiently explored. In this study, we perform an in-depth examination of how semantic encoders shape latent representation learning for the autoencoders. Our findings reveal a fundamental trade-off: while semantic encoders generate latent spaces enriched with visual semantics, their high level of abstraction makes it challenging to capture fine-grained geometric relationships, thereby requiring larger models and longer training for convergence. To address this issue, we build upon recent advances in representation learning that enable the joint modeling of both semantic abstraction and geometric detail. This leads to a Semantic Auto-Encoder (S-AE) that achieves state-of-the-art performance, combining superior reconstruction quality and discriminative capability. Specifically, with S-AE, we are able to provide a unified latent space that achieves 0.06 FID for image reconstruction and 81.9% classification accuracy on ImageNet, set a state-of-the-art benchmark. Codes and model weights will be made publicably 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:0a8af8196ccefcaf7e617e490292aaadCVF · CVPR 2026

SketchAssist: A Practical Assistant for Semantic Edits and Precise Local Redrawing

Zou, Han, Zhang, Yan, Yu, Ruiqi, Xie, Cong, Huang, Jie, Zhan, Zhenpeng

原文摘要

Sketch editing requires jointly handling high-level semantic changes and precise local redrawing, a combination that is particularly challenging for sparse, style-sensitive line art. Unlike natural images, sketches rely on minimal visual cues, making it difficult for existing methods to reconcile global semantic modifications with fine-grained structural control while preserving overall coherence. We present SketchAssist, an interactive sketch assistant that unifies instruction-guided editing with line-guided region redrawing, enabling efficient and controllable sketch manipulation while preserving overall composition. To support this task, we introduce a controllable data generation pipeline that constructs structured edit sequences with precise attribute variations and maintains structural alignment across multi-step modifications, while expanding stylistic diversity via style-preserving transformations. Building on this data, SketchAssist adopts a unified framework based on DiT, using a multi-channel input representation to encode sketches, masks, and guidance signals within a single interface. To further handle different editing modes, we integrate a Task-guided Mixture-of-Experts (T-MoE) into LoRA layers, enabling adaptive control over semantic and structural guidance. Extensive experiments demonstrate state-of-the-art performance on both tasks, achieving strong instruction adherence and improved structural and style consistency compared to recent methods. Together, our method provide a practical and controllable solution for sketch editing.

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:19a4ea644eb4725c1fac1a7b473d430cCVF · CVPR 2026

SD-FSMIS: Adapting Stable Diffusion for Few-Shot Medical Image Segmentation

Li, Meihua, Zhang, Yang, He, Weizhao, Qu, Hu, Li, Yisong

原文摘要

Few-Shot Medical Image Segmentation (FSMIS) aims to segment novel object classes in medical images using only minimal annotated examples, addressing the critical challenges of data scarcity and domain shifts prevalent in medical imaging. While Diffusion Models (DM) excel in visual tasks, their potential for FSMIS remains largely unexplored. We propose that the rich visual priors learned by large-scale DMs offer a powerful foundation for a more robust and data-efficient segmentation approach. In this paper, we introduce SD-FSMIS, a novel framework designed to effectively adapt the powerful pre-trained Stable Diffusion (SD) model for the FSMIS task. Our approach repurposes its conditional generative architecture by introducing two key components: a Support-Query Interaction (SQI) and a Visual-to-Textual Condition Translator (VTCT). Specifically, SQI provides a straightforward yet powerful means of adapting SD to the FSMIS paradigm. The VTCT module translates visual cues from the support set into an implicit textual embedding that guides the diffusion model, enabling precise conditioning of the generation process. Extensive experiments demonstrate that SD-FSMIS achieves competitive results compared to state-of-the-art methods in standard settings. Surprisingly, it also demonstrated excellent generalization ability in more challenging cross-domain scenarios. These findings highlight the immense potential of adapting large-scale generative models to advance data-efficient and robust medical image segmentation.

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:1c2974b72bc5c085bd666dd6ab3d4138CVF · CVPR 2026

Egocentric Visibility-Aware Human Pose Estimation

Dai, Peng, Zhang, Yu, Yiqiang, Feng, Fan, Zhen, Zhang, Yang

原文摘要

Egocentric human pose estimation (HPE) using a head-mounted device is crucial for various VR and AR applications, but it faces significant challenges due to keypoint invisibility. Nevertheless, none of the existing egocentric HPE datasets provide keypoint visibility annotations, and the existing methods often overlook the invisibility problem, treating visible and invisible keypoints indiscriminately during estimation. As a result, their capacity to accurately predict visible keypoints is compromised. In this paper, we first present Eva-3M, a large-scale egocentric visibility-aware HPE dataset comprising over 3.0M frames, with 435K of them annotated with keypoint visibility labels. Additionally, we augment the existing EMHI dataset with keypoint visibility annotations to further facilitate the research in this direction. Furthermore, we propose EvaPose, a novel egocentric visibility-aware HPE method that explicitly incorporates visibility information to enhance pose estimation accuracy. Extensive experiments validate the significant value of ground-truth visibility labels in egocentric HPE settings, and demonstrate that our EvaPose achieves state-of-the-art performance in both Eva-3M and EMHI datasets.

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:23872da820c47a98423cad5bc5367a5fCVF · CVPR 2026

GeodesicNVS: Probability Density Geodesic Flow Matching for Novel View Synthesis

Wang, Xuqin, Wu, Tao, Zhang, Yanfeng, Liu, Lu, Sun, Mingwei, Wang, Yongliang, Zeller, Niclas, Cremers, Daniel

原文摘要

Recent advances in generative modeling have substantially enhanced novel view synthesis, yet maintaining consistency across viewpoints remains challenging. Diffusion-based models rely on stochastic noise-to-data transitions, which obscure deterministic structures and yield inconsistent view predictions. We advocate a Data-to-Data Flow Matching framework that learns deterministic transformations between paired views, enhancing view-consistent synthesis through explicit data coupling. Building on this, we propose Probability Density Geodesic Flow Matching (PDG-FM), which aligns interpolation trajectories with density-based geodesics of a data manifold. To enable tractable geodesic estimation, we employ a teacher-student framework that distills density-based geodesic interpolants into an efficient ambient-space predictor. Empirically, our method surpasses diffusion-based baselines on Objaverse and GSO30 datasets, demonstrating improved structural coherence and smoother transitions across views. These results highlight the advantages of incorporating data-dependent geometric regularization into deterministic flow matching for consistent novel view generation.

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:299570bd5c5f4672af6083dab0600b07CVF · CVPR 2026

HAVE-Bench: Hierarchical Audio-Visual Evaluation from Perception to Interaction

Zhong, Muyan, Cui, Erfei, Xing, Sen, Wang, Weiyun, Wu, Wen, Hu, Yuchen, Zhang, Yanting, Hu, Xiaowei 等 11 位作者

原文摘要

Multimodal large language models (MLLMs) have expanded from vision-language systems to include audio, unlocking new capabilities in cross-modal reasoning and interaction. To address the limitation that existing benchmarks focus mainly on perception tasks and lack a unified cognitive evaluation framework, we propose Hierarchical Audio-Visual Evaluation Benchmark (HAVE-Bench). It systematically evaluates the audio-related capabilities of MLLMs along a three-level cognitive hierarchy: Perception, Reasoning, and Interaction, utilizing 2,451 curated samples and manually annotated multi-turn interaction-level tasks. Experiments using this unified framework reveal significant gaps in existing models at the reasoning and interaction levels, with speech-driven visual question answering (VQA) performance significantly lagging behind the text-image setting. These findings underscore the urgency of enhancing models' handling of long and complex audio and facilitating the transfer of reasoning capabilities from the vision-text to the audio-visual domain.

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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:2eeaac6633fe8f25207823c9eb452324CVF · CVPR 2026

End-to-End Hyper-Relational Information Extraction for Engineering Diagrams via Dynamically Tokenized Relation Transformer

Bai, Tianyou, Zhang, Yan-Ming, Zhang, Zixiang, Zhou, Jibin, Yin, Fei, Liu, Cheng-Lin

原文摘要

Engineering diagrams are the core carriers of technical information in industrial contexts, where the pressing demand for their digitization from industrial sectors has driven great advancements in related research domains. However, existing research still suffers from three limitations. Firstly, the detection of symbols, lines, and texts typically involves multiple independent models, resulting in cumbersome workflows. In addition, high-resolution diagrams often impose an excessive computational cost on existing models. Moreover, parsing frameworks solely based on object detection can merely localize component positions, yet fail to capture the topological connection semantics and structured knowledge among components, thus offering limited convenience for industrial applications. To address these issues, we propose an end-to-end information extraction framework based on the Dynamically Tokenized Relation Transformer (DTRT), which can dynamically reduce received image tokens, filter redundant information, and efficiently extract structural knowledge to construct hyper-relational knowledge graphs. We practiced our model on piping and instrumentation diagrams (P&IDs) and electrical diagrams (EDs): the former are widely used in chemical engineering enterprises, while the latter are employed to describe circuit systems. DTRT achieves an R@1000 accuracy of 94.84% on PIDs and R@200 accuracy of 92.52% on EDs with a significantly reduced computational cost. Our code is available at https://github.com/Tianyou-Bai/DTRT-Diagram-Parsing-in-Scene-Graph.

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:3e2ec5e4c12393b9417ee8dda44a49a9CVF · CVPR 2026

Wavelet-based Frame Selection by Detecting Semantic Boundary for Long Video Understanding

Chen, Wang, Zeng, Yuhui, Luo, Yongdong, Xie, Tianyu, Lin, Luojun, Ji, Jiayi, Zhang, Yan, Zheng, Xiawu

原文摘要

Frame selectoin is crucial due to high frame redundancy and limited context windows when applying Large Vision-Language Models (LVLMs) to long videos. Current methods typically select frames with high relevance to a given query, resulting a disjointed set of frames that disregard the narrative structure of video. In this paper, we introduce Wavelet-based Frame Selection by Detecting Semantic Boundary (WFS-SB), a training-free framework that presents a new perspective: effective video understanding hinges not only on high relevance but, more importantly, on capturing semantic shifts--pivotal moments of narrative change that are essential to comprehending the holistic storyline of video. However, a direct detection of abrupt changes in the query-frame similarity signal is often unreliable due to high-frequency noise arising from model uncertainty and transient visual variations.To address this, we leverage the wavelet transform, which provides an ideal solution through its multi-resolution analysis in both time and frequency domains. By applying this transform, we decompose the noisy signal into multiple scales and extract a clean semantic change signal from the coarsest scale. We identify the local extrema of this signal as semantic boundaries, which segment the video into coherent clips. Building on this, WFS-SB comprises a two-stage strategy: first, adaptively allocating a frame budget to each clip based on a composite importance score; and second, within each clip, employing the Maximal Marginal Relevance approach to select a diverse yet relevant set of frames. Extensive experiments show that WFS-SB significantly boosts LVLM performance, e.g., improving accuracy by 5.5% on VideoMME, 9.5% on MLVU, and 6.2% on LongVideoBench, consistently outperforming state-of-the-art methods. Our code is available at https://github.com/MAC-AutoML/WFS-SB.

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:41d47b380ae70bad182aec8ae91b02d6CVF · CVPR 2026

Geo2: Geometry-Guided Cross-view Geo-Localization and Image Synthesis

Zhang, Yancheng, Zhang, Xiaohan, Sun, Guangyu, Lyu, Zonglin, Wshah, Safwan, Chen, Chen

原文摘要

Cross-view geo-spatial learning consists of two important tasks: Cross-View Geo-Localization (CVGL) and Cross-View Image Synthesis (CVIS), both of which rely on establishing geometric correspondences between ground and aerial views. Recent Geometric Foundation Models (GFMs) have demonstrated strong capabilities in extracting generalizable 3D geometric features from images, but their potential in cross-view geo-spatial tasks remains underexplored. In this work, we present Geo^2, a unified framework that leverages Geometric priors from GFMs (e.g., VGGT) to jointly perform geo-spatial tasks, CVGL and bidirectional CVIS. Despite the 3D reconstruction ability of GFMs, directly applying them to CVGL and CVIS remains challenging due to the large viewpoint gap between ground and aerial imagery. We propose GeoMap, which embeds ground and aerial features into a shared 3D-aware latent space, effectively reducing cross-view discrepancies for localization. This shared latent space naturally bridges cross-view image synthesis in both directions. To exploit this, we propose GeoFlow, a flow-matching model conditioned on geometry-aware latent embeddings. We further introduce a consistency loss to enforce latent alignment between the two synthesis directions, ensuring bidirectional coherence. Extensive experiments on standard benchmarks, including CVUSA, CVACT, and VIGOR, demonstrate that Geo^2 achieves state-of-the-art performance in both localization and synthesis, highlighting the effectiveness of 3D geometric priors for cross-view geo-spatial learning.

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:4600ca46c747652825fc88d4597515c7CVF · CVPR 2026

Robustness Under Data Scarcity: Few-Shot Continual Adversarial Training for Evolving Threats

Wang, Wenxuan, Wang, Chenglei, Yan, Chengzhi, Qian, Xuelin, Zhang, Yanning

原文摘要

Deep learning models remain highly vulnerable to evolving adversarial attacks. While existing continual adversarial training approaches often assume abundant adversarial data at each stage, real-world scenarios frequently involve limited data availability. This paper addresses the setting of Few-shot Continual Adversarial Training, where only a small number of adversarial examples are available per stage, presenting major challenges in achieving robust generalization and mitigating catastrophic forgetting. To tackle these challenges, we propose a novel continual adversarial training framework that incorporates three key components: (i) an Adversarial Margin loss that explicitly pushes clean samples away from decision boundaries to enhance feature discrimination; (ii) a Gaussian mixture model Prototype Replay strategy that synthesizes representative pseudo-features to preserve knowledge of past adversarial domains; and (iii) a Multi-Domain Balanced loss that guides updates to stabilize learning across diverse attack distributions. Extensive experiments on ImageNet-1K and CIFAR-100 demonstrate that our approach consistently outperforms state-of-the-art methods in both clean and robust accuracy across a variety of adversarial settings. The code will be released.

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:4ef623a654fba7f8e1f4dc8d7ca5b763CVF · CVPR 2026

RAG-TP: A General Framework for Vehicle Trajectory Prediction via Retrieval-Augmented Generation

Wang, Ziyi, Zhang, Yang, Tang, Guijian, Zhang, Chao, Zhang, Shibo, Li, Xueqiong, Yang, Shaowu

原文摘要

Vehicle trajectory prediction is critical for safe and efficient autonomous driving. However, its generalization and scalability are hindered by heavy reliance on real-time, online priors. To break this bottleneck, we introduce RAG-TP, a framework reframing the problem from relying on uncertain online perception to retrieving from a large-scale, structured knowledge base. RAG-TP enhances inference-time predictions by dynamically querying a heterogeneous knowledge base rich with scene topologies and motion patterns, using retrieved historical experiences as priors. We further design a dynamic fusion module based on a novel Retrieval-Driven Mixture-of-Experts (MoE). Unlike conventional parametric designs, this mechanism dynamically treats retrieved knowledge units as experts, weighting and integrating them via cross-attention to generate a dense context for final multi-modal trajectory decoding. By decoupling online inference from offline knowledge, this approach grounds predictions in a vast structured database, mitigating model hallucination, compensating for unreliable priors, and significantly enhancing robustness and domain adaptation. Extensive experiments show RAG-TP achieves excellent performance in map-based and map-free settings, demonstrating highly competitive results against specialized map-free methods while performing on par with state-of-the-art (SOTA) map-based models. It demonstrates significant advantages, particularly in cross-domain and zero-shot generalization. Our work provides a promising technical pathway toward building scalable and robust prediction systems for autonomous driving.

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:506147ebd7e0a4a2530927f5b68f7ee1CVF · CVPR 2026

MMBench-GUI: A Unified Hierarchical Evaluation Framework for Multi-Platform GUI Agents

Wang, Xuehui, Wu, Zhenyu, Xie, JingJing, Ding, Zichen, Yang, Bowen, Li, Zehao, Liu, Zhaoyang, Li, Qingyun 等 25 位作者

原文摘要

We introduce MMBench-GUI, a hierarchical benchmark for evaluating GUI automation agents across Windows, macOS, Linux, iOS, Android, and Web. The benchmark spans four levels: Content Understanding, Element Grounding, Task Automation, and Task Collaboration, covering essential skills for GUI agents. To assess both effectiveness and efficiency, we further propose the Efficiency-Quality-Aware (EQA) metric, which measures task success alongside action redundancy. Extensive evaluations reveal that precise visual grounding is the critical determinant of performance, underscoring the advantages of modular designs with specialized grounding modules. Moreover, all agents suffer from substantial inefficiencies, frequently completing tasks with excessive steps despite eventual success. Performance also degrades on complex or cross-application tasks, exposing weaknesses in memory, planning, and adaptive reasoning. By providing broad coverage, standardized protocols, and novel metrics, MMBench-GUI establishes the first comprehensive foundation for advancing GUI agent research.

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:5880173b1ac498bccada83a7aac31d9eCVF · CVPR 2026

Language Does Matter for Cross-Domain Few-Shot Visual Feature Enhancement

Zhou, Fei, Zhang, Xiwen, Qiu, Qingqing, Zhang, Lei, Wei, Wei, Ding, Chen, Zhang, Yi, Li, Liang 等 10 位作者

原文摘要

Cross-domain few-shot image interpretation (CD-FSII) has been significantly advanced by fine-tuning pre-trained visual feature models using limited labeled samples in target domains. However, profound cross-domain distribution discrepancies, along with inherent conflicts between extensive object visual appearance variations and limited annotations, trap those existing pure visual feature representations into some non-transferable short-cut patterns, thus degrading their cross-domain generalization capacity. To mitigate this problem, we present a simple yet effective cross-modal visual feature enhancement framework which primarily contributes in the following three aspects. 1) We make the first attempt to introduce linguistic descriptions of image attributes to regulate the pre-trained visual feature model for specific target image adaptation. Specifically, image-level attributes (e.g., object appearance in individual images) and domain-level attributes (e.g., overall style and background characteristics of the dataset) are extracted using a pre-trained image captioning model and a large language model (LLM), respectively, to construct comprehensive linguistic characterizations. 2) A lightweight residual cross-attention scheme is developed to seamlessly embed linguistic descriptions of image attributes into visual feature representations, thereby compensating for the limitations of purely visual cues in capturing cross-domain transferable high-level semantic characteristics. 3) The proposed framework is task-agnostic and can be seamlessly integrated with off-the-shelf pre-trained visual feature models. It demonstrates superior generalization performance compared to several state-of-the-art methods across multiple CD-FSII benchmarks, including image classification, semantic segmentation, and object detection. We will release all code and data to facilitate further research.

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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:68cb53b093cd1ee229d2e62e7a40747eCVF · CVPR 2026

FARMER: Flow AutoRegressive Transformer over Pixels

Zheng, Guangting, Zhao, Qinyu, Yang, Tao, Xiao, Fei, Lin, Zhijie, Wu, Jie, Deng, Jiajun, Zhang, Yanyong 等 9 位作者

原文摘要

Directly modeling the explicit likelihood of the raw data distribution is a key topic in the machine learning area, which achieves the scaling success in Large Language Models by autoregressive modeling. However, continuous AR modeling over visual pixel data suffers from extremely long sequences and high-dimensional spaces. In this paper, we present FARMER, a novel end-to-end generative framework that unifies Normalizing Flows (NFs) and Autoregressive (AR) models for tractable likelihood estimation and high-quality image synthesis directly from raw pixels. FARMER employs an invertible autoregressive flow to transform images into latent sequences, whose distribution is implicitly modeled by an autoregressive model. To address the redundancy and complexity in pixel-level modeling, we propose a self-supervised dimension reduction scheme that partitions NF latent channels into informative and redundant groups, enabling more effective and efficient AR modeling. Furthermore, we design a one-step distillation scheme to significantly accelerate inference speed and introduce a resampling-based classifier-free guidance algorithm to boost image generation quality. Extensive experiments demonstrate that FARMER achieves competitive performance compared to existing pixel-based generative models while providing exact likelihoods and scalable training.

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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:708e9ef879b0ed72afa99f38bf5d5839CVF · CVPR 2026

Bridging the Modality Gap in Compositional Zero-Shot Learning via Sparse Alignment and Unimodal Memory Bank

Zhang, Yang, Chi, Zhixiang, Yan, Xudong, Wang, Yang, Feng, Songhe

原文摘要

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object compositions with learned primitives (attribute and object) knowledge from seen compositions. While previous approaches gain their notable performance through the powerful cross-modal alignment of CLIP, they often overlook the modality gap, an inherent constraint stemming from information-imbalanced training data. In this work, we propose SAM, a novel \underline \text S parse \underline \text A lignment and Unimoal \underline \text M emory Bank to effectively bridging modality gap for CZSL. Specifically, we conduct sparse alignment that links textual representations directly to their semantically pertinent visual patches. This direct linking serves to prune redundant visual data and counter the information imbalance in image-text pairs. Subsequently, with the sparsely aligned visual information as its guidance, the visual adaptive condensation module adaptively fuses these critical cues into a unified representation. Finally, we introduce a dynamically updated memory bank that stores samples from both seen and unseen compositions. This bank serves a dual purpose: it bypasses the modality gap through visual-only classification and concurrently strengthens generalization to unseen compositions. Experiments on three benchmarks demonstrate that our method gains significant improvements over CLIP-based methods under closed-world and open-world settings.

cs.CV

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

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UniVBench: Towards Unified Evaluation for Video Foundation Models

Wei, Jianhui, Zhang, Xiaotian, Li, Yichen, Wang, Yuan, Zhang, Yan, Chen, Ziyi, Tang, Zhihang, Xu, Wei 等 9 位作者

原文摘要

Video foundation models aim to integrate video understanding, generation, editing, and instruction following within a single framework, making them a central direction for next-generation multimodal systems. However, existing evaluation benchmarks remain fragmented and limited in scope, as they each target a single task, rely on task-specific metrics, and typically use short or simple video clips. As a result, they do not capture the unified capabilities that these models are designed to deliver. To address this gap, we introduce UniVBench, a benchmark purpose-built for evaluating video foundation models across four core abilities: video understanding, video generation, video editing, and a newly proposed task, video reconstruction, which assesses how faithfully a model can reproduce video content it has encountered. Our benchmark substantially expands the complexity of evaluation by incorporating 200 high-quality, diverse and multi-shot videos, each paired with detailed captions, multi-format editing instructions, and reference images. All videos are entirely human-created and carefully validated, offering richer cinematic information than prior benchmarks. In addition, we develop a unified agentic evaluation system (UniV-Eval) that standardizes prompting, instruction parsing, and scoring across all tasks, enabling fair, scalable, and reproducible comparisons of unified video models. By grounding evaluation in instruction-based multi-shot video tasks, UniVBench provides the first framework for measuring the integrated capabilities that video foundation models aim to achieve. Extensive human annotations ensure our evaluation aligns with human judgment, enabling rigorous assessment and accelerating progress toward robust video intelligence.

cs.CV · cs.AI

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

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SAGE: Style-Adaptive Generalization for Privacy-Constrained Semantic Segmentation Across Domains

Li, Qingmei, Zhang, Yang, Zhang, Peifeng, Fu, Haohuan, Zheng, Juepeng

原文摘要

Domain generalization for semantic segmentation aims to mitigate the degradation in model performance caused by domain shifts. However, in many real-world scenarios, we are unable to access the model parameters and architectural details due to privacy concerns and security constraints. Traditional fine-tuning or adaptation is hindered, leading to the demand for input-level strategies that can enhance generalization without modifying model weights. To this end, we propose a Style-Adaptive GEneralization framework (SAGE), which improves the generalization of frozen models under privacy constraints. SAGE learns to synthesize visual prompts that implicitly align feature distributions across styles instead of directly fine-tuning the backbone. Specifically, we first utilize style transfer to construct a diverse style representation of the source domain, thereby learning a set of style characteristics that can cover a wide range of visual features. Then, the model adaptively fuses these style cues according to the visual context of each input, forming a dynamic prompt that harmonizes the image appearance without touching the interior of the model. Through this closed-loop design, SAGE effectively bridges the gap between frozen model invariance and the diversity of unseen domains. Extensive experiments on five benchmark datasets demonstrate that SAGE achieves competitive or superior performance compared to state-of-the-art methods under privacy constraints and outperforms full fine-tuning baselines in all settings.

cs.CV

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

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Guardians of the Hair: Rescuing Soft Boundaries in Depth, Stereo, and Novel Views

Zhang, Xiang, Zhang, Yang, Mehl, Lukas, Gross, Markus, Schroers, Christopher

原文摘要

Soft boundaries, like thin hairs, are commonly observed in natural and computer-generated imagery, but they remain challenging for 3D vision due to the ambiguous mixing of foreground and background cues. This paper introduces Guardians of the Hair (HairGuard), a framework designed to recover fine-grained soft boundary details in 3D vision tasks. Specifically, we first propose a novel data curation pipeline that leverages image matting datasets for training and design a depth fixer network to automatically identify soft boundary regions. With a gated residual module, the depth fixer refines depth precisely around soft boundaries while maintaining global depth quality, allowing plug-and-play integration with state-of-the-art depth models. For view synthesis, we perform depth-based forward warping to retain high-fidelity textures, followed by a generative scene painter that fills disoccluded regions and eliminates redundant background artifacts within soft boundaries. Finally, a color fuser adaptively combines warped and inpainted results to produce novel views with consistent geometry and fine-grained details. Extensive experiments demonstrate that HairGuard achieves state-of-the-art performance across monocular depth estimation, stereo image/video conversion, and novel view synthesis, with significant improvements in soft boundary regions.

cs.CV

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

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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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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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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.

cs.CV

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

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OrienPose: Orientation-Guided Novel View Synthesis for Single-Image Unseen Object Pose Estimation

Liu, Yating, Qi, Zhaoshuai, Zou, Yang, Yang, Yongnan, Zhang, Shizhou, Zhang, Yanning

原文摘要

Estimating the 3D pose of unseen objects from a single image remains a fundamental yet challenging problem in computer vision, especially under a CAD model-free setting.Pioneering attempts address this issue by matching templates generated through Novel View Synthesis (NVS), which essentially aims to learn the geometric transformation from a reference to a target view. While promising, these methods can only approximate this transformation under pixel-level supervision, as the starting orientation remains undefined. In the absence of explicit geometric constraints to verify the correctness of the predicted transformation, existing methods often synthesize novel views with geometry-distorted structures or severely blurred local textures, leading to unreliable template matching and suboptimal pose estimation results. To this end, we propose OrienPose, a novel object pose estimation framework via orientation-aware NVS from a single image. Specifically, we introduce the Orientation-Aware Guidance, which explicitly injects object orientation cues into the reference latent embedding to enhance orientation awareness during viewpoint transformation. We also introduce an orientation consistency loss that supervises viewpoint transformation at the geometric level, establishing sufficient supervision for explicit and geometry-consistent transformation guidance beyond pixel-level similarity. This loss justifies estimating the reference orientation rather than using its ground-truth pose, thereby ensuring the alignment of coordinate domains between the injected and supervised priors. Extensive experiments demonstrate that OrienPose achieves state-of-the-art performance in single-view unseen object pose estimation and impressive robustness to image degradations. Our code is available at https://github.com/pubyLu/OrienPose.

cs.CV

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

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Bilevel Layer-Positioning LoRA for Real Image Dehazing

Zhang, Yan, Ma, Long, Feng, Yuxin, Huang, Zhe, Zhou, Fan, Su, Zhuo

原文摘要

Learning-based real image dehazing methods have achieved notable progress, yet they still face adaptation challenges in diverse real haze scenes. These challenges mainly stem from the lack of effective unsupervised mechanisms for unlabeled data and the heavy cost of full model fine-tuning. To address these challenges, we propose the haze-to-clear text-directed loss that leverages CLIP's cross-modal capabilities to reformulate real image dehazing as a semantic alignment problem in latent space, thereby providing explicit unsupervised cross-modal guidance in the absence of reference images. Furthermore, we introduce the Bilevel Layer-positioning LoRA (BiLaLoRA) strategy, which learns both the LoRA parameters and automatically search the injection layers, enabling targeted adaptation of critical network layers. Extensive experiments demonstrate our superiority against state-of-the-art methods on multiple real-world dehazing benchmarks.

cs.CV

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

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fMRI-LM: Towards a Universal Foundation Model for Language-Aligned fMRI Understanding

Wei, Yuxiang, Zhang, Yanteng, Xiao, Xi, Qian, Chengxuan, Wang, Tianyang, Calhoun, Vince D.

原文摘要

Recent advances in multimodal large language models (LLMs) have enabled unified reasoning across images, audio, and video, but extending such capability to brain imaging remains largely unexplored. Bridging this gap is essential to link neural activity with semantic cognition and to develop cross-modal brain representations. To this end, we present fMRI-LM, a foundational model that bridges functional MRI (fMRI) and language through a three-stage framework. In Stage 1, we learn a neural tokenizer that maps fMRI into discrete tokens embedded in a language-consistent space. In Stage 2, a pretrained LLM is adapted to jointly model fMRI tokens and text, treating brain activity as a sequence that can be temporally predicted and linguistically described. To overcome the lack of natural fMRI-text pairs, we construct a large descriptive corpus that translates diverse imaging-based features into structured textual descriptors, capturing the low-level organization of fMRI signals. In Stage 3, we perform multi-task, multi-paradigm instruction tuning to endow fMRI-LM with high-level semantic understanding, supporting diverse downstream applications. Across various benchmarks, fMRI-LM achieves strong zero-shot and few-shot performance, and adapts efficiently with parameter-efficient tuning (LoRA), establishing a scalable pathway toward a language-aligned, universal model for structural and semantic understanding of fMRI.

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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RenderFlow: Single-Step Neural Rendering via Flow Matching

Zhang, Shenghao, Liu, Runtao, Schroers, Christopher, Zhang, Yang

原文摘要

Conventional physically-based rendering (PBR) pipelines generate photorealistic images through computationally expensive light transport simulations. Although recent deep learning approaches leverage diffusion model priors with geometry buffers (G-buffers) to produce visually compelling results without explicit scene geometry or light simulation, they remain constrained by two major limitations. First, the iterative nature of the diffusion process introduces substantial latency. Second, the inherent stochasticity of these generative models compromises physical accuracy and temporal consistency. In response to these challenges, we propose a novel, end-to-end, deterministic single-step neural rendering framework RenderFlow built upon a flow matching paradigm. To further strengthen both rendering quality and generalization, we propose an efficient and effective module for sparse keyframe guidance. Our method significantly accelerates the rendering process and, by optionally incorporating sparsely rendered keyframes as guidance, enhances both the physical plausibility and overall visual quality of the output. The resulting pipeline achieves near real-time performance with photorealistic rendering quality, effectively bridging the gap between the efficiency of modern generative models and the precision of traditional physically based rendering. Furthermore, we demonstrate the versatility of our framework by introducing a lightweight, adapter-based module that efficiently repurposes the pretrained forward model for the inverse rendering task of intrinsic decomposition.

cs.CV

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

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Understanding and Mitigating Hallucinations in Multimodal Chain-of-Thought Models

Ma, Ji, Suo, Wei, Wang, Peng, Zhang, Yanning

原文摘要

Multimodal Chain-of-Thought (MCoT) models have demonstrated impressive capability in complex visual reasoning tasks. Unfortunately, recent studies reveal that they suffer from severe hallucination problems due to diminished visual attention during the generation process.However, visual attention decay is a well-studied problem in Large Vision-Language Models (LVLMs). Considering the fundamental differences in reasoning processes between MCoT models and traditional LVLMs, we raise a basic question: Whether MCoT models have unique causes of hallucinations? To answer this question, we systematically investigate the hallucination patterns of MCoT models and find that fabricated texts are primarily generated in associative reasoning steps, which we term divergent thinking. Leveraging these insights, we introduce a simple yet effective strategy that can effectively localize divergent thinking steps and intervene in the decoding process to mitigate hallucinations. Extensive experiments show that our method outperforms existing methods by a large margin. More importantly, our proposed method can be conveniently integrated with other hallucination mitigation methods and further boost their performance. The code is publicly available at https://github.com/ASGO-MM/MCoT-hallucination.

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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Proxy-GS: Unified Occlusion Priors for Training and Inference in Structured 3D Gaussian Splatting

Gao, Yuanyuan, Gong, Yuning, Liu, Yifei, Li, Jingfeng, Xu, Dan, Zhang, Yanci, Zhang, Dingwen, Sun, Xiao 等 9 位作者

原文摘要

3D Gaussian Splatting (3DGS) has emerged as an efficient approach for photorealistic rendering. Recent MLP-based variants further improve visual fidelity but introduce substantial decoding overhead during rendering. To reduce the computational cost, several pruning strategies and level-of-detail (LOD) techniques have been introduced to reduce the number of Gaussian primitives in large-scale scenes. However, our analysis reveals that significant redundancy still remains due to the lack of occlusion awareness. In this work, we propose Proxy-GS, a novel pipeline that uses a proxy representation to introduce occlusion awareness for Gaussians from arbitrary views. At the core of our approach is a fast proxy system capable of producing precise occlusion depth maps at a resolution of 1000 x 1000 in under 1 ms. This proxy serves two roles. First, it guides the culling of anchors and Gaussians to accelerate rendering. Second, it guides densification toward scene surfaces during training, reducing inconsistencies in occluded regions and thereby improving rendering quality. In heavily occluded scenarios such as the MatrixCity Streets dataset, Proxy-GS achieves more than a 2.5x speedup over Octree-GS while also improving rendering quality.

cs.CV

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

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Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos

Jiang, Songtao, Song, Sibo, Zhou, Chenyi, Wang, Yuan, Chen, Ruizhe, Guan, Tongkun, Luo, Ruilin, Zhang, Yan 等 15 位作者

原文摘要

The transition from image to video understanding requires vision-language models (VLMs) to shift from recognizing static patterns to reasoning over temporal dynamics such as motion trajectories, speed changes, and state transitions. Yet current post-training methods fall short due to two critical limitations: (1) existing datasets often lack temporal-centricity, where answers can be inferred from isolated keyframes rather than requiring holistic temporal integration; (2) training data generated by proprietary models contains systematic errors in fundamental temporal perception, such as confusing motion directions or misjudging speeds. We introduce SynRL, a post-training framework that teaches models temporal primitives, the fundamental building blocks of temporal understanding including direction, speed, and state tracking. Our key insight is that these abstract primitives, learned from programmatically generated synthetic videos, transfer effectively to real-world scenarios. We decompose temporal understanding into short-term perceptual primitives (speed, direction) and long-term cognitive primitives (state tracking, retrodictive inference), constructing 7.7K CoT and 7K RL samples with ground-truth frame-level annotations through code-based video generation. Despite training on simple geometric shapes, SynRL achieves substantial improvements across 15 benchmarks spanning temporal grounding, complex reasoning, and general video understanding. Remarkably, our 7.7K synthetic CoT samples outperform Video-R1's 165K real-world samples. We attribute this to fundamental temporal skills--such as tracking frame-by-frame changes and comparing velocity--that transfer effectively from abstract synthetic patterns to complex real-world scenarios. This establishes a new paradigm for video post-training: video temporal learning through carefully designed synthetic data provides a more cost-efficient scaling path. Code is released on https://github.com/jiangsongtao/Synthetic-Video.

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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