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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:07fe83d3f6e46b74d35f597826c8441eCVF · CVPR 2026

EVA: Efficient Reinforcement Learning for End-to-End Video Agent

Zhang, Yaolun, Wang, Ruohui, Wang, Jiahao, Tang, Yepeng, Zheng, Xuanyu, Duan, Haonan, Lu, Hao, Deng, Hanming 等 9 位作者

原文摘要

Video understanding with multimodal large language models (MLLMs) remains challenging due to the long token sequences of videos, which contain extensive temporal dependencies and redundant frames.Existing approaches typically treat MLLMs as passive recognizers, processing entire videos or uniformly sampled frames without adaptive reasoning.Recent agent-based methods introduce external tools, yet still depend on manually designed workflows and perception-first strategies, resulting in inefficiency on long videos. We present EVA, an Efficient Reinforcement Learning framework for End-to-End Video Agent, which enables planning-before-perception through iterative summary-plan-action-reflection reasoning.EVA autonomously decides what to watch, when to watch, and how to watch, achieving query-driven and efficient video understanding.To train such agents, we design a simple yet effective three-stage learning pipeline--comprising supervised fine-tuning (SFT), Kahneman-Tversky Optimization (KTO), and Group Relative Policy Optimization (GRPO)--that bridges supervised imitation and reinforcement learning.We further construct high-quality datasets for each stage, supporting stable and reproducible training.We evaluate EVA on six video understanding benchmarks, demonstrating its comprehensive capabilities. Compared with existing baselines, EVA achieves a substantial improvement of 6--12% over general MLLM baselines and a further 1--3% gain over prior adaptive agent methods.

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:0e6854b7c0cf65590cdd739578a4d830CVF · CVPR 2026

OmniGen2: Towards Instruction-Aligned Multimodal Generation

Wu, Chenyuan, Wang, Jiahao, Zheng, Pengfei, Yan, Ruiran, Xiao, Shitao, Luo, Xin, Wang, Yueze, Li, Wanli 等 24 位作者

原文摘要

Multimodal generative models can process instructions in various modalities and demonstrate outstanding performance across a wide range of image generation tasks. However, their robustness in complex real-world scenarios remains limited due to insufficient generalized instruction alignment. We introduce OmniGen2, a unified multimodal generator designed to follow complex, fine-grained instructions. Our core contribution is a two-stage design that first builds a strong, world-knowledge-grounded foundation model and then aligns it using a progressive, multi-task instruction tuning strategy. The foundation model features a streamlined architecture with decoupled decoding for versatile multimodal generation and a novel positional encoding scheme to improve learning efficiency. We ground this model in real-world knowledge using large-scale data construction pipelines. Building on this foundation, we propose a progressive, reinforcement-based alignment process. This phase carefully schedules training tasks and reward signals to foster cross-task knowledge transfer, significantly improving the model's instruction-following capabilities. Our models demonstrate competitive performance on standard benchmarks and our dedicated in-context generation benchmark, OmniContext. We have released our models, code, benchmark, and training datasets at https://github.com/VectorSpaceLab/OmniGen2.

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:0fffc628efcef48ba3e05f3db5524ff5CVF · CVPR 2026

LagerNVS: Latent Geometry for Fully Neural Real-time Novel View Synthesis

Szymanowicz, Stanislaw, Chen, Minghao, Wang, Jianyuan, Rupprecht, Christian, Vedaldi, Andrea

原文摘要

Recent work has shown that neural networks can perform 3D tasks such as Novel View Synthesis (NVS) without explicit 3D reconstruction. Even so, we argue that strong 3D inductive biases are still helpful in the design of such networks. We show this point by introducing LagerNVS, an encoder-decoder neural network for NVS that builds on `3D-aware' latent features. The encoder is initialized from a 3D reconstruction network pre-trained using explicit 3D supervision. This is paired with a lightweight decoder, and trained end-to-end with photometric losses. LagerNVS achieves state-of-the-art deterministic feed-forward Novel View Synthesis (including 31.4 PSNR on Re10k), with and without known cameras, renders in real time, generalizes to in-the-wild data, and can be paired with a diffusion decoder for generative extrapolation. See szymanowiczs.github.io/lagernvs for code, models, and examples.

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:1243829dd36fc8ba201ab7402a22408aCVF · CVPR 2026

PRISM: Learning a Shared Primitive Space for Transferable Skeleton Action Representation

Yang, Di, Wang, Yaohui, Shao, Shuai, Brémond, François, Wang, Jiangtao

原文摘要

Real-world human action understanding remains challenging due to long-tailed label distributions, compositional motion patterns, and viewpoint variations. Existing skeleton-based methods often lack a structured and transferable representation of motion, and task-specific models for generation, classification, and detection are usually trained independently, resulting in fragmented pipelines and limited cross-task generalization. We present PRISM, a PRImitive-centric Skeleton Modeling framework that learns a shared motion representation from a motion generation objective and transfers it to perception tasks. PRISM represents each action sequence as a trajectory in a primitive coefficient space, which captures how a set of learned atomic motion primitives contribute to the observed motion. A structured decomposition module learns this representation in a physically grounded and view-invariant manner via motion generation. Instead of enforcing joint or unified training across tasks, PRISM provides a single primitive-centric representation that can be sequentially transferred to downstream classification and frame-wise detection through lightweight task heads. This representation introduces structure, compositionality, and improved generalization across distinct supervisions. PRISM consistently improves performance on long-tailed and multi-label datasets and enables interpretable reasoning over compositional and rare actions. Extensive experimental results show that the structured primitive space serves as a transferable and robust foundation for diverse action understanding tasks in real-world 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:12a8555d2067e20139ec4189130182ceCVF · CVPR 2026

Think Visually, Reason Textually: Vision-Language Synergy in Abstract Reasoning

Zhang, Beichen, Zang, Yuhang, Dong, Xiaoyi, Cao, Yuhang, Duan, Haodong, Lin, Dahua, Wang, Jiaqi

原文摘要

Abstract reasoning from minimal examples remains a core unsolved problem for frontier foundation models such as GPT-5. These models still fail to infer structured transformation rules from a handful of examples, which is a key hallmark of human intelligence. The Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI) provides a rigorous testbed for this capability, demanding conceptual rule induction and transfer to novel tasks. Most existing methods treat ARC-AGI as a purely textual reasoning task, overlooking the fact that humans rely heavily on visual abstraction when solving such puzzles. However, our experiments reveal a paradox: naively rendering ARC-AGI grids as images degrades performance due to imprecise rule execution. This leads to our central hypothesis that vision and language possess complementary strengths across distinct reasoning stages: vision supports global pattern abstraction and verification, whereas language specializes in symbolic rule formulation and precise execution. Building on this insight, we introduce two synergistic strategies: (1) Vision-Language Synergy Reasoning (VLSR), which decomposes ARC-AGI into modality-aligned subtasks; and (2) Modality-Switch Self-Correction (MSSC), which leverages vision to verify text-based reasoning for intrinsic error correction. Extensive experiments demonstrate that our approach yields up to a 4.33% improvement over text-only baselines across diverse flagship models and multiple ARC-AGI tasks. Our findings suggest that unifying visual abstraction with linguistic reasoning is a crucial step toward achieving generalizable, human-like intelligence.

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:13ec645d3214d12910541a8e3c6b6ea4CVF · CVPR 2026

AnyID: Ultra-Fidelity Universal Identity-Preserving Video Generation from Any Visual References

Wang, Jiahao, Sheng, Hualian, Cai, Sijia, Yang, Yuxiao, Zhang, Weizhan, Yan, Caixia, Deng, Bing, Ye, Jieping

原文摘要

Identity-preserving video generation offers powerful tools for creative expression, allowing users to customize videos featuring their beloved characters. However, prevailing methods are typically designed and optimized for a single identity reference. This underlying assumption restricts creative flexibility by inadequately accommodating diverse real-world input formats. Relying on a single source also constitutes an ill-posed scenario, causing an inherently ambiguous setting that makes it difficult for the model to faithfully reproduce an identity across novel contexts. To address these issues, we present AnyID, an ultra-fidelity identity-preservation video generation framework that features two core contributions. First, we introduce a scalable omni-referenced architecture that effectively unifies heterogeneous identity inputs (e.g., faces, portraits, and videos) into a cohesive representation. Second, we propose a primary-referenced generation paradigm, which designates one reference as a canonical anchor and uses a novel differential prompt to enable precise, attribute-level controllability. We conduct training on a large-scale, meticulously curated dataset to ensure robustness and high fidelity, and then perform a final fine-tuning stage using reinforcement learning. This process leverages a preference dataset constructed from human evaluations, where annotators performed pairwise comparisons of videos based on two key criteria: identity fidelity and prompt controllability. Extensive evaluations validate that AnyID achieves ultra-high identity fidelity as well as superior attribute-level controllability across different task settings. Project page: https://johnneywang.github.io/AnyID-webpage.

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:1ed56889f44b74e581cf8f85da15abe8CVF · CVPR 2026

SAMTok: Representing Any Mask with Two Words

Zhou, Yikang, Zhang, Tao, Gong, Dengxian, Wu, Yuanzheng, Tian, Ye, Wang, Haochen, Yuan, Haobo, Wang, Jiacong 等 16 位作者

原文摘要

Pixel-wise capabilities are essential for building interactive intelligent systems. However, pixel-wise multi-modal LLMs (MLLMs) remain difficult to scale due to complex region-level encoders, specialized segmentation decoders, and incompatible training objectives. To address these challenges, we present SAMTok, a discrete mask tokenizer that converts any region mask into two special tokens and reconstructs the mask from these tokens with high fidelity. By treating masks as a new language, SAMTok enables base MLLMs (such as the QwenVL series) to learn pixel-wise capabilities through standard next-token prediction and simple reinforcement learning, without architectural modifications and specialized loss design. SAMTok builds on SAM2 and is trained on 209M diverse masks using a mask encoder and residual vector quantizer to produce discrete, compact, and information-rich tokens. With 5M SAMTok-formatted mask understanding and generation data samples, QwenVL-SAMTok attains state-of-the-art or comparable results on region captioning, region VQA, grounded conversation, referring segmentation, scene graph parsing, and multi-round interactive segmentation. We further introduce a textual answer-matching reward that enables efficient reinforcement learning for mask generation, delivering substantial improvements on GRES and GCG benchmarks. Our results demonstrate a scalable and straightforward paradigm for equipping MLLMs with strong pixel-wise capabilities.

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:22e230a058402f0dab5492c9a3941e26CVF · CVPR 2026

Beyond Binary Contrast: Modeling Continuous Skeleton Action Spaces with Transitional Anchors

Feng, Yingjie, Wang, Yi, Wang, Jiaze, Liu, Anfeng, Tian, Zhuotao

原文摘要

Self-supervised contrastive learning has emerged as a powerful paradigm for skeleton-based action recognition by enforcing consistency in the embedding space. However, existing methods rely on binary contrastive objectives that overlook the intrinsic continuity of human motion, resulting in fragmented feature clusters and rigid class boundaries. To address these limitations, we propose TranCLR, a Transitional anchor-based Contrastive Learning framework that captures the continuous geometry of the action space. Specifically, the proposed Action Transitional Anchor Construction (ATAC) explicitly models the geometric structure of transitional states to enhance the model's perception of motion continuity. Building upon these anchors, a Multi-Level Geometric Manifold Calibration (MGMC) mechanism is introduced to adaptively calibrate the action manifold across multiple levels of continuity, yielding a smoother and more discriminative representation space. Extensive experiments on the NTU RGB+D, NTU RGB+D 120 and PKU-MMD datasets demonstrate that TranCLR achieves superior accuracy and calibration performance, effectively learning continuous and uncertainty-aware skeleton representations. Code will be made 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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:259d88b47a31b1168731d5f1964a0ecfCVF · CVPR 2026

Eliminate Distance Differences Induced by Backdoor Attacks: Layer-Selective Training and Clipping to Mask Backdoor Models

Li, Xuzeng, Zhang, Tao, Tang, Xiangyun, Wang, Jiacheng, Wang, Jian, Kang, Jiawen, Liu, Jiqiang, Han, Zhen 等 10 位作者

原文摘要

Federated learning (FL) enables a central server to collaboratively train a global model with multiple clients while preserving data privacy. However, the distributed nature of FL makes the paradigm vulnerable to backdoor attacks, as proved by numerous recent studies. Although existing studies improve the effectiveness of backdoor attacks through optimized triggers, they have two limitations: (1) they ignore the heterogeneous contribution of individual model layers to the success of a backdoor; (2) they induce conspicuous differences between backdoor and clean models in the early stages of poisoning. The limitations cause backdoor models to exhibit significant discrepancies from clean models, making them easily detectable. To fill these gaps, we propose LaySelFL, a novel layer-selective method to eliminate distance differences induced by the backdoor to conceal attacks in FL. Our central insight is that different layers contribute unequally to backdoor attacks, by localizing poisoning to layers that are most sensitive to backdoor objectives, an attacker can reduce the model differences substantially between the backdoor and clean models. Concretely, LaySelFL identifies sensitive layers via both dynamic and static evaluations of parameter differences between backdoor and benign models, and then applies a targeted training protocol and a regularized loss that constrains differences from the global model in each round. Finally, LaySelFL performs clipping on non-poisoning layers to further mask residual differences introduced by the attack. This strategy yields a more covert and resilient backdoor attack. Extensive experiments show that LaySelFL increases the effectiveness of attacks by 25% and reduces the effectiveness of defense methods to 4%.

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:2770050cc360d6db960cc7880351e480CVF · CVPR 2026

Refacade: Editing Object with Given Reference Texture

Huang, Youze, Ruan, Penghui, Zi, Bojia, Qi, Xianbiao, Wang, Jianan, Xiao, Rong

原文摘要

Recent advances in diffusion models have brought remarkable progress in image and video editing, yet some tasks remain underexplored. In this paper, we extend Object Retexture into video domain, which transfers local textures from a reference object to a target object in images or videos. To perform this task, a straightforward solution is to use ControlNet conditioned on the source structure and the reference texture. However, this approach suffers from limited controllability due to two reasons: conditioning on the raw reference image introduces unwanted structural information, and this method fails to disentangle visual texture and structure information of the source. To address this problem, we proposed a method, namely Refacade, that consists of two key designs to achieve precise and controllable texture transfer in both images and videos. First, we employ a texture remover trained on paired textured/untextured 3D mesh renderings to remove appearance information while preserving geometry and motion of source videos. Second, we disrupt the reference's global layout using a jigsaw permutation, encouraging the model to focus on local texture statistics rather than global layout of object. Extensive experiments demonstrate superior visual quality, precise editing, and controllability, outperforming strong baselines in both quantitative and human evaluations.

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:34c70944e4fda5268fd42e0ec9b90352CVF · CVPR 2026

Beyond [CLS] Token: Query-Driven Token-Level Forgery Purification for Generalizable Deepfake Detection

Wang, Changshuo, Wang, Jiangming, Zhang, Ke-Yue, Yao, Taiping, Ding, Shouhong, Wang, Shunli, Yi, Ran, Ma, Lizhuang

原文摘要

We investigate state-of-the-art deepfake detectors that leverage ViT-based vision foundation models and discover that the [CLS] token suffers from the Pre-trained Information Bias (PIB), i.e., it tends to mainly focus on global semantics due to the knowledge dominated by pre-trained model parameters, while struggling to emphasize subtle local forgery cues. To overcome this limitation, one potential way is incorporating the token-level features to reform a detection-specific token. To this end, we propose Query-Driven Token-Level Forgery Purification (QTFP) framework to better capture local forgery traces without losing useful pre-trained prior. Specifically, we introduce randomly initialized, learnable query tokens independent of the backbone and prior knowledge, which effectively aggregate multi-patch evidence into a global token for detection. To make query tokens focus on meaningful regions, we propose a theoretical fake-likelihood contrastive learning loss, which employs a weighting strategy to highlight significant fake regions while diminishing real-like patch impact. Using SNR theory, we verify that the designed weight is both reliable and informative. To further maintain useful authentic information, a real-attention alignment constraint is applied to query tokens. These designs go beyond relying solely on the [CLS] token by jointly reorganizing real and fake information across all tokens, which successfully enhance detector robustness. Extensive experiments on diverse datasets demonstrate the effectiveness of our method.

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:3d5b405f1b72db2e239f08609cbcd67fCVF · CVPR 2026

Beyond Rule-Based Agents: Active Markov Games for Realistic Multi-Agent Interaction in Autonomous Driving

Gui, Yuan, Luo, Hongchen, Wang, Jiao, Qu, Liqi

原文摘要

Current research in autonomous driving heavily relies on large-scale driving datasets for model fitting or trial-and-error learning strategies in simulation environments. However, these approaches suffer from limited behavioral diversity and fail to cover complex edge-case interactions. To address these limitations, we model the driving environment as an Active Markov Game (AMG) and introduce a multi-agent co-evolutionary training framework for more realistic interactive learning. The AMG formulation extends traditional Markov games by explicitly making state transitions and rewards dependent on the evolving strategies of the agents, thus capturing the interactive dynamics and strategic coupling between the ego vehicle and surrounding agents. Building on this, our multi-agent co-evolutionary training mechanism jointly optimizes the ego vehicle's policy and a diverse pool of opponent strategies, allowing all agents to adapt to each other's behaviors during training. This game-theoretic approach produces a robust ego agent capable of handling diverse, non-stationary driving strategies, overcoming the "non-responsive opponent" limitation found in prior methods. In CARLA simulations of unsignalized intersections and long-tail scenarios, our method performs exceptionally well, achieving near-perfect success rates (98%), and significantly outperforming state-of-the-art baselines such as PPO, DDPG, and IPPO in terms of generalization, safety margins, and control smoothness. These results demonstrate that our approach substantially enhances the robustness, safety, and strategic adaptability of autonomous driving in complex multi-agent environments.

cs.CV · cs.MA

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:3fab9797acc5b225c085597fd8dd46c4CVF · CVPR 2026

ArtiMuse: Fine-Grained Image Aesthetics Assessment with Joint Scoring and Expert-Level Understanding

Cao, Shuo, Ma, Nan, Li, Jiayang, Li, Xiaohui, Shao, Lihao, Zhu, Kaiwen, Zhou, Yu, Pu, Yuandong 等 15 位作者

原文摘要

The rapid advancement of educational applications, artistic creation, and AI-generated content (AIGC) technologies has substantially increased practical requirements for comprehensive Image Aesthetics Assessment (IAA), particularly demanding methods capable of delivering both quantitative scoring and professional understanding. Multimodal Large Language Model (MLLM)-based IAA methods demonstrate stronger perceptual and generalization capabilities compared to traditional approaches, yet they suffer from modality bias (score-only or text-only) and lack fine-grained attribute decomposition, thereby failing to support further aesthetic assessment. In this paper, we present: (1) ArtiMuse, an innovative MLLM-based IAA model with Joint Scoring and Expert-Level Understanding capabilities; (2) ArtiMuse-10K, the first expert-curated IAA dataset comprising 10,000 images spanning 5 main categories and 15 subcategories, each annotated by professional experts with 8-dimensional attributes analysis and a holistic score. Both the model and dataset will be made public.

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:41ebc90ef7ffaffcfac55944c558ebcfCVF · CVPR 2026

C^2FG: Control Classifier-Free Guidance via Score Discrepancy Analysis

Gao, Jiayang, Zheng, Tianyi, Zou, Jiayang, Yang, Fengxiang, Liu, Shice, Fan, Luyao, Zhang, Zheyu, Zhang, Hao 等 12 位作者

原文摘要

Classifier-Free Guidance (CFG) is a cornerstone of modern conditional diffusion models, yet its reliance on the fixed or heuristic dynamic guidance weight is predominantly empirical and overlooks the inherent dynamics of the diffusion process. In this paper, we provide a rigorous theoretical analysis of the Classifier-Free Guidance. Specifically, we establish strict upper bounds on the score discrepancy between conditional and unconditional distributions at different timesteps based on the diffusion process.This finding explains the limitations of fixed-weight strategies and establishes a principled foundation for time-dependent guidance. Motivated by this insight, we introduce **Control Classifier-Free Guidance (C^2FG)**, a novel, training-free, and plug-in method that aligns the guidance strength with the diffusion dynamics via an exponential decay control function. Extensive experiments demonstrate that C^2FG is effective and broadly applicable across diverse generative tasks, while also exhibiting orthogonality to existing strategies.

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:486f0dcd9e84c1583508db7d222a0a96CVF · CVPR 2026

Beyond Text: Visual Description Assembly by Probabilistic Model for CLIP-based Weakly Supervised Semantic Segmentation

Qiu, Xianglin, Wang, Jian, Wang, Xiaolei, Zhang, Zhen, Xiao, Jimin

原文摘要

Contrastive Language-Image Pre-training (CLIP) offers a new paradigm for Weakly Supervised Semantic Segmentation (WSSS) by generating Class Activation Maps (CAMs) from text-image alignment. Existing methods primarily rely on hand-crafted templates or general attribute descriptions generated by a large language model to construct text prototypes for querying visual features. However, these strategies faces two major limitations: the inherent modality gap in CLIP prevents text prototypes achieving tight alignment with visual features; and their static text prototypes cannot adaptively respond to target instances that exhibit diverse visual attributes. To address these challenges, our key insight is to directly construct instance-specific visual description prototype as query, thereby bypassing the suboptimal static text description optimization. To this end, we propose the Visual Description Assembly (VDA) framework. It employs a probabilistic model to map complex CLIP visual features into a structured latent space. This latent space allows us to explicitly disentangle and aggregate varied visual attributes, and then dynamically assemble them into instance-specific visual prototypes. Furthermore, to enhance the robustness of this prototype, we adaptively incorporate the semantically stable text prototype into it as the final query for generating superior CAMs. Experimental results show our method outperforms existing baselines, achieving state-of-the-art performance on WSSS benchmarks.

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:52d0cfb544bc9802ebc48caecd12492eCVF · CVPR 2026

LitePT: Lighter Yet Stronger Point Transformer

Yue, Yuanwen, Robert, Damien, Wang, Jianyuan, Hong, Sunghwan, Wegner, Jan Dirk, Rupprecht, Christian, Schindler, Konrad

原文摘要

Modern neural architectures for 3D point cloud processing contain both convolutional layers and attention blocks, but the best way to assemble them remains unclear. We analyse the role of different computational blocks in 3D point cloud networks and find an intuitive behaviour: convolution is adequate to extract low-level geometry at high-resolution in early layers, where attention is expensive without bringing any benefits; attention captures high-level semantics and context in low-resolution, deep layers more efficiently. Guided by this design principle, we propose a new, improved 3D point cloud backbone that employs convolutions in early stages and switches to attention for deeper layers. To avoid the loss of spatial layout information when discarding redundant convolution layers, we introduce a novel, parameter-free 3D positional encoding, PointROPE. The resulting LitePT model has 3.6x fewer parameters, runs 2x faster, and uses 2x less memory than the state-of-the-art Point Transformer V3, but nonetheless matches or outperforms it on a range of tasks and datasets.

cs.CV

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

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ARM-Thinker: Reinforcing Multimodal Generative Reward Models with Agentic Tool Use and Visual Reasoning

Ding, Shengyuan, Fang, Xinyu, Liu, Ziyu, Zang, Yuhang, Cao, Yuhang, Zhao, Xiangyu, Duan, Haodong, Dong, Xiaoyi 等 13 位作者

原文摘要

Reward models are critical for aligning vision-language systems with human preferences, yet current approaches suffer from hallucination, weak visual grounding, and an inability to use tools for verification, limiting their reliability on complex multimodal reasoning tasks.We present **ARM-Thinker**, an **A**gentic multimodal **R**eward **M**odel that autonomously invokes external tools (e.g., image cropping, doc page retrieval) to ground judgments in verifiable evidence, replacing static, non-interactive reward scoring.This enables the model to verify fine-grained visual details, cross-reference multi-page evidence, and validate reasoning claims, which are capabilities absent in existing reward models.We train ARM-Thinker with multi-stage reinforcement learning, jointly optimizing tool-calling decisions and judgment accuracy.To evaluate agentic reward modeling, we introduce **ARMBench-VL**, comprising three benchmarks that assess fine-grained visual grounding (image-level tools), multi-page document understanding (retrieval tools), and instruction following (text-level verification).ARM-Thinker achieves +16.2% average improvement on reward modeling benchmarks, +9.6% on tool-use tasks, and outperforms baselines on multimodal math and logical reasoning benchmarks.Our results demonstrate that agentic capabilities significantly enhance both accuracy and interpretability of reward models.

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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Dynamics-Aware Preference Optimization for Vision-Language Models

Zhang, Jusheng, Cai, Kaitong, Yang, Jing, Wang, Jian, Wang, Keze

原文摘要

Preference-based finetuning of vision-language models (VLMs) is notoriously unstable, as trivially wrong negatives inject uninformative gradients that distort optimization and degrade calibration. This work revisits this issue through the lens of learning dynamics and identifies a core pathology, the squeezing effect, where easy negatives retain large, misaligned gradients despite having negligible loss.To address this, we propose Cooling-Weighted Direct Preference Optimization (CW-DPO), a two-stage framework that first smooths and then stabilizes the alignment process. Stage 1 employs a constrained SFT phase with low-weight "gentle negatives" to regularize overconfident distributions and flatten the loss landscape. Stage 2 introduces a competence-aware cooling weight that adaptively scales negative gradients according to the model's average per-token log-probability, suppressing uninformative updates while emphasizing hard, on-policy contrasts. This dynamics-aware weighting effectively mitigates the squeezing effect and enables smoother convergence.Extensive experiments on mainstream benchmarks--including COCO, Flickr30k, NoCaps, MMMU, and MMBench1.1--show that CW-DPO achieves state-of-the-art performance, for example +3.4 CIDEr over PPO and +2.4% absolute accuracy on MMMU, while improving calibration and halving convergence steps. This demonstrates that smoothing before cooling constitutes a simple yet general principle for robust VLM preference optimization.

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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ELV-Halluc: Benchmarking Semantic Aggregation Hallucinations in Video Understanding

Lu, Hao, Wang, Jiahao, Zhang, Yaolun, Wang, Ruohui, Zheng, Xuanyu, Tang, Yepeng, Lin, Dahua, Lu, Lewei

原文摘要

We revisit video hallucination in multimodal large language models (Video-MLLMs) from a semantic aggregation perspective. While prior work attributes hallucinations to language priors, missing frames, or visual encoder biases, these explanations overlook errors arising during the aggregation of correct frame-level semantics into event-level interpretations. We term this phenomenon Semantic Aggregation Hallucination (SAH), which becomes increasingly prevalent in complex, multi-event video understanding tasks with rich temporal dependencies. To systematically study SAH, we introduce ELV-Halluc, the first benchmark designed for fine-grained evaluation of semantic aggregation errors. Our experiments reveal that SAH correlates with both semantic complexity and rapid semantic transitions. We further propose mitigation strategies: improved positional encoding preserves temporal structure, and reinforcement learning such as DPO enhances the model's ability to distinguish semantics within and across events. Using a curated 8K adversarial video-text pair dataset, our approach achieves consistent gains across benchmarks, including a 27.7% reduction in SAH rate on ELV-Halluc and Video-MME. Data and code are available at https://github.com/hlsv02/ELV-Halluc.

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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InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields

Yu, Hao, Lin, Haotong, Wang, Jiawei, Li, Jiaxin, Wang, Yida, Zhang, Xueyang, Wang, Yue, Zhou, Xiaowei 等 10 位作者

原文摘要

Existing depth estimation methods are fundamentally limited to predicting depth on discrete image grids. Such representations restrict their scalability to arbitrary output resolutions and hinder the geometric detail recovery. This paper introduces InfiniDepth, which represents depth as neural implicit fields. Through a simple yet effective local implicit decoder, we can query depth at continuous 2D coordinates, enabling arbitrary-resolution and fine-grained depth estimation. To better assess our method's capabilities, we curate a high-quality 4K synthetic benchmark from five different games, spanning diverse scenes with rich geometric and appearance details. Extensive experiments demonstrate that InfiniDepth achieves state-of-the-art performance on both synthetic and real-world benchmarks across relative and metric depth estimation tasks, particularly excelling in fine-detail regions. It also benefits the task of novel view synthesis under large viewpoint shifts, producing high-quality results with fewer holes and artifacts. Code and data will be made 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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Sensor2Sensor: Cross-Embodiment Sensor Conversion for Autonomous Driving

Wang, Jiahao, Sun, Bo, Bai, Yijing, Casser, Vincent, Peng, Songyou, Zhu, Zehao, Shih, Meng-Li, Masotto, Xander 等 15 位作者

原文摘要

Robust training and validation of Autonomous Driving Systems (ADS) require massive, diverse datasets. Proprietary data collected by Autonomous Vehicle (AV) fleets, while high-fidelity, are limited in scale, diversity of sensor configurations, as well as geographic and long-tail-behavioral coverage. In contrast, in-the-wild data from sources like dashcams offers immense scale and diversity, capturing critical long-tail scenarios and novel environments. However, this unstructured, in-the-wild video data is incompatible with ADS expecting structured, multi-modal sensor inputs for validation and training. To bridge this data gap, we propose Sensor2Sensor, a novel generative modeling paradigm that translates in-the-wild monocular dashcam videos into a high-fidelity, multi-modal sensor suite (AV logs) comprising multi-view camera images and LiDAR point clouds. A core challenge is the lack of paired training data. We address this by converting real AV logs into dashcam-style videos via 4D Gaussian Splatting (4DGS) reconstruction and novel-view rendering. Sensor2Sensor then utilizes a diffusion architecture to perform the generative conversion. We perform comprehensive quantitative evaluations on the fidelity and realism of the generated sensor data. We demonstrate Sensor2Sensor's practical utility by converting challenging in-the-wild internet and dashcam footage into realistic, multi-modal data formats, further unlocking vast external data sources for AV development.

cs.CV

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

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Beyond Multiple Choice: Verifiable OpenQA for Robust Vision-Language RFT

Liu, Yesheng, Li, Hao, Xu, Haiyu, Pei, Baoqi, Wang, Jiahao, Zhao, Mingxuan, Zheng, Jing-Shu, He, Zheqi 等 12 位作者

原文摘要

Multiple-choice question answering (MCQA) has been a popular format for evaluating and reinforcement fine-tuning (RFT) of modern multimodal language models. Its constrained output format allows for simplified, deterministic automatic verification.However, we find that the options may leak exploitable signals, which makes the accuracy metrics unreliable for indicating real capabilities and encourages explicit or implicit answer guessing behaviors during RFT. We propose ReVeL (Rewrite and Verify by LLM), a framework that rewrites multiple-choice questions into open-form questions while keeping answers verifiable whenever possible. The framework categorizes questions according to different answer types, apply different rewriting and verification schemes, respectively. When applied for RFT, we converted 20k MCQA examples and use GRPO to finetune Qwen2.5-VL models. Models trained on ReVeL-OpenQA match MCQA accuracy on multiple-choice benchmarks and improve OpenQA accuracy by about six percentage points, indicating better data efficiency and more robust reward signals than MCQA-based training. When used for evaluation, ReVeL also reveals up to 20 percentage points of score inflation in MCQA benchmarks (relative to OpenQA), improves judging accuracy, and reduces both cost and latency. We will release code and data publicly.

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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UniChange: Unifying Change Detection with Multimodal Large Language Model

Zhang, Xu, Li, Danyang, Dong, Xiaohang, Wu, Tianhao, Yu, Hualong, Wang, Jianye, Li, Qicheng, Li, Xiang

原文摘要

Change detection (CD) is a fundamental task for monitoring and analysing land cover dynamics. While recent high performance models and high quality datasets have significantly advanced the field, a critical limitation persists. Current models typically acquire limited knowledge from single-type annotated data and cannot concurrently leverage diverse binary change detection (BCD) and semantic change detection (SCD) datasets. This constraint leads to poor generalisation and limited versatility. The recent advancements in Multimodal Large Language Models (MLLMs) introduce new possibilities for a unified CD framework. We leverage the language priors and unification capabilities of MLLMs to develop UniChange, the first MLLM-based unified change detection model. UniChange integrates generative language abilities with specialised CD functionalities. We introduce three special tokens: [T1], [T2], and [CHANGE], utilising their embeddings as the key to query variations. This approach successfully accommodates both BCD and SCD tasks. Furthermore, UniChange utilises text prompts to guide the identification of change categories, eliminating the reliance on predefined classification heads. This design allows UniChange to effectively acquire knowledge from multi-source datasets, even when their class definitions conflict. Experiments on four public benchmarks (WHU-CD, S2Looking, LEVIR-CD+, and SECOND) demonstrate SOTA performance, achieving IoU scores of 90.41, 53.04, 78.87, and 57.62, respectively, surpassing all previous methods. The code is available at https://github.com/NKU-HLT/UniChange.

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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See What We Cannot See: A Geo-guided Reasoning Benchmark for Object Counting under Adverse Earth Observation Conditions

Wang, Jiayi, Tan, Zhihong, Wei, Hongchen, Yang, Daiqin, Chen, Zhenzhong

原文摘要

Object counting in remote sensing imagery becomes challenging when visual cues are obscured by clouds, fog, shadows, or low-light conditions. Yet earth observation inherently provides complementary geo-modalities, including land use and map, which offer stable structural and contextual priors that remain available when appearance cues fail. In this paper, we introduce GROC, the first large-scale dataset Geo-guided Reasoning in Object Counting under adverse earth observation conditions. GROC contains 1.2 million point annotations over 14K images, each aligned with 3 modalities that preserve original geospatial information. We also provide a data engine to collect a large-scale object counting dataset with multiple geo-modalities, realistic degradations, and reliable annotations. We further present an counting agent that adaptively leverages geo-modalities to produce reliable estimates. Extensive experiments show that existing models struggle to "see" through adverse conditions, whereas geo-modalities improve robustness. GROC establishes the first benchmark that explicitly challenges models to see what they cannot see, charting a new direction for geo-guided amodal reasoning in earth observation.

cs.CV

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

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HandX: Scaling Bimanual Motion and Interaction Generation

Zhang, Zimu, Zhang, Yucheng, Xu, Xiyan, Wang, Ziyin, Xu, Sirui, Zhou, Kai, Zhou, Bing, Guo, Chuan 等 11 位作者

原文摘要

Synthesizing human motion has advanced rapidly, yet realistic hand motion and bimanual interaction remain underexplored. Whole-body models often miss the fine-grained cues that drive dexterous behavior, finger articulation, contact timing, and inter-hand coordination, and existing resources lack high-fidelity bimanual sequences that capture nuanced finger dynamics and collaboration. To fill this gap, we present HandX, a unified foundation spanning data, annotation, and evaluation. We consolidate and filter existing datasets for quality, and collect a new motion-capture dataset targeting underrepresented bimanual interactions with detailed finger dynamics. For scalable annotation, we introduce a decoupled paradigm that extracts representative motion features, e.g., contact events and finger flexion, and then leverages reasoning from large language models to produce fine-grained, semantically rich descriptions aligned with these features. Building on the resulting data and annotations, we benchmark diffusion and autoregressive models with versatile conditioning modes. Experiments demonstrate high-quality dexterous motion generation, supported by our newly proposed hand-focused metrics. We further observe clear scaling trends: larger models trained on larger, higher-quality datasets produce more semantically coherent bimanual motion. Our dataset is released to support future 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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SegMo: Co-Designing Content-Aware Sparsity and Locally-Cohesive Segment Parallelism for Efficient VLM Inference

Li, Haojuan, Tang, Ruohan, Cheng, Dongzhou, Zhang, Zongpu, Li, Jian, Wang, Jiaqi

原文摘要

Video Large Language Models (VideoLLMs) face a fundamental performance bottleneck: the token explosion intrinsic to video inputs. The resulting O(N^2) prefill cost makes conventional Transformer inference prohibitively expensive at scale. Existing attempts fall into a hard accuracy-latency dilemma: naive sparsification risks losing essential temporal-spatial context, whereas naive parallelization introduces substantial communication and memory overhead. To overcome this impasse, we propose SegMo, a unified framework based on algorithm-system co-design that jointly optimizes what to compute (sparsification) and how to compute it (parallelism). Driven by the empirical discovery of Local Cohesion in VideoLLM attention, SegMo integrates two key components: (1) Content-Aware Sparsification (CAS): A lightweight, hierarchical algorithm that first employs Query Relevance for scene-level assessment, and then uses Temporal Redundancy for intra-scene static redundancy pruning, to generate a precise, non-uniform computation load, ensuring accuracy. (2) Locally-Cohesive Segment Parallelism (LSP): A novel paradigm that leverages attention locality to partition the video at scene boundaries, using a lightweight Global Context Injection mechanism to replace the massive communication and memory overheads of global attention. SegMo was validated across LVBench, LongVideoBench, and Video-MME. Our CAS module improved accuracy by up to 12.00%. When integrated with LSP, the full system (CAS + LSP) achieved a peak prefill acceleration of 3.55x, while still maintaining a significant accuracy gain of up to 8.31%.

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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I2I-Bench: A Comprehensive Benchmark Suite for Image-to-Image Editing Models

Wang, Juntong, Wang, Jiarui, Duan, Huiyu, Kang, Jiaxiang, Zhai, Guangtao, Min, Xiongkuo

原文摘要

Image editing models are advancing rapidly, yet comprehensive evaluation remains a significant challenge. Existing image editing benchmarks generally suffer from limited task scopes, insufficient evaluation dimensions, and heavy reliance on manual annotations, which significantly constrain their scalability and practical applicability. To address this, we propose I2I-Bench, a comprehensive benchmark for image-to-image editing models, which features (i) diverse tasks, encompassing 10 task categories across both single-image and multi-image editing tasks, (ii) comprehensive evaluation dimensions, including 30 decoupled and fine-grained evaluation dimensions with automated hybrid evaluation methods that integrate specialized tools and large multimodal models (LMMs), and (iii) rigorous alignment validation, justifying the consistency between our benchmark evaluations and human preferences.Using I2I-Bench, we benchmark numerous mainstream image editing models, investigating the gaps and trade-offs between editing models across various dimensions. We will open-source all components of I2I-Bench to facilitate future research.

cs.CV

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

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AGENTSAFE: Benchmarking the Safety of Embodied Agents on Hazardous Instructions

Ying, Zonghao, Wang, Le, Xiao, Yisong, Wang, Jiakai, Ma, Yuqing, Guo, Jinyang, Yin, Zhenfei, Zhang, Mingchuan 等 10 位作者

原文摘要

The integration of vision-language models (VLMs) is driving a new generation of embodied agents capable of operating in human-centered environments. However, as deployment expands, these systems face growing safety risks, particularly when executing hazardous instructions. Current safety evaluation benchmarks remain limited: they cover only narrow scopes of hazards and focus primarily on final outcomes, neglecting the agent's full perception-planning-execution process and thereby obscuring critical failure modes. Therefore, we present SAFE, a benchmark for systematically assessing the safety of embodied VLM agents on hazardous instructions. SAFE comprises three components: SAFE-THOR, an extensible adversarial simulation sandbox with a universal adapter that maps high-level VLM outputs to low-level embodied controls, supporting diverse agent workflow integration; SAFE-VERSE, a risk-aware task suite inspired by Asimov's Three Laws of Robotics, comprising 45 adversarial scenarios, 1,350 hazardous tasks, and 9,900 instructions that span risks to humans, environments, and agents; and SAFE-DIAGNOSE, a multi-level and fine-grained evaluation protocol measuring agent performance across perception, planning, and execution. Applying SAFE to nine state-of-the-art VLMs and two embodied agent workflows, we uncover systematic failures in translating hazard recognition into safe planning and execution. Our findings reveal fundamental limitations in current safety alignment and demonstrate the necessity of a comprehensive, multi-stage evaluation for developing safer embodied intelligence.

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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OmniFM: Toward Modality-Robust and Task-Agnostic Federated Learning for Heterogeneous Medical Imaging

Liu, Meilin, Wang, Jiaying, Shan, Jing

原文摘要

Federated learning (FL) has become a promising paradigm for collaborative medical image analysis, yet existing frameworks remain tightly coupled to task-specific backbones and are fragile under heterogeneous imaging modalities. Such constraints hinder real-world deployment, where institutions vary widely in modality distributions and must support diverse downstream tasks. To address this limitation, we propose OmniFM,a modality- and task-agnostic FL framework that unifies training across classification,segmentation, super-resolution, visual question answering,and multimodal fusion without re-engineering the optimization pipeline. OmniFM builds on a key frequency-domain insight: low-frequency spectral components exhibit strong cross-modality consistency and encode modality-invariant anatomical structures. Accordingly, OmniFM integrates(i) Global Spectral Knowledge Retrieval to inject global frequency priors, (ii) Embedding-wise Cross-Attention Fusion to align representations, and (iii) Prefix-Suffix Spectral Prompting to jointly condition global and personalized cues, together regularized by a Spectral-Proximal Alignment objective that stabilizes aggregation. Experiments on real-world datasets show that OmniFM consistently surpasses state-of-the-art FL baselines across intra- and cross-modality heterogeneity, achieving superior results under both fine-tuning and training-from-scratch setups.

cs.CV

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

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