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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:0ab2e6716bbf73216910b7b8b6ecfdd4CVF · CVPR 2026

Enhancing Spatial Understanding in Image Generation via Reward Modeling

Tang, Zhenyu, Feng, Chaoran, Deng, Yufan, Wu, Jie, Li, Xiaojie, Wang, Rui, Chen, Yunpeng, Zhou, Daquan

原文摘要

Recent progress in text-to-image generation has greatly advanced visual fidelity and creativity, but it has also imposed higher demands on prompt complexity--particularly in encoding intricate spatial relationships. In such cases, achieving satisfactory results often requires multiple sampling attempts.To address this challenge, we introduce a novel method that strengthens the spatial understanding of current image generation models. We first construct the SpatialReward-Dataset with over 80k preference pairs. Building on this dataset, we build SpatialScore, a reward model designed to evaluate the accuracy of spatial relationships in text-to-image generation, achieving performance that even surpasses leading proprietary models on spatial evaluation.We further demonstrate that this reward model effectively enables online reinforcement learning for the complex spatial generation. Extensive experiments across multiple benchmarks show that our specialized reward model yields significant and consistent gains in spatial understanding for image generation. All models and datasets 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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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:0cfbfa40b3c78b6a75506fd00104afa7CVF · CVPR 2026

PhyOceanCast: Global Ocean Forecasting with Physics-Informed Diffusion

Li, Qixiu, Zhu, Xiang, Li, Xiaoyong, Xu, Xiaolong

原文摘要

Ocean dynamics drive global climate patterns and extreme weather events, making accurate spatiotemporal forecasting essential for climate monitoring and marine operations. Traditional Global Ocean Forecasting Systems (GOFSs) offer high accuracy predictions, yet remain computationally expensive and fail to fully leverage growing historical data. Recent deep learning models have achieved notable success, but still face three fundamental challenges: (1) they homogenize ocean variables despite strong physical coupling via equation-of-state relationships; (2) they neglect spherical geometry, resulting in severe distortions at high latitudes; and (3) they struggle to model multi-scale temporal dynamics. We introduce PhyOceanCast, a physics-informed diffusion model that overcomes these limitations through two key innovations. First, the Spherical Graph Attention Network for Multi-scale Ocean Coupling (SGAN-MOC) preserves spherical topology while enabling cross-variable interactions via heterogeneous encoding and k-hop-constrained attention. Second, the Physics-Informed Wavelet Temporal Coherence (PWTC) module that decomposes ocean dynamics across multiple scales with advection-diffusion constraints. PhyOceanCast forecasts 145 ocean variables, including temperature, salinity, and velocity fields, across 36 depth levels plus sea surface height. Extensive experiments demonstrate superior performance over diffusion, transformer, and hybrid baselines, promising a new paradigm for global ocean canonical variable forecasting.

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:0ff4940dfc41d241922b8db41cb06eb1CVF · CVPR 2026

BiPreManip: Learning Affordance-Based Bimanual Preparatory Manipulation through Anticipatory Collaboration

Shen, Yan, Jiang, Feng, He, Zichen, Li, Xiaoqi, Liu, Yuchen, Li, Zhiyu, Wu, Ruihai, Dong, Hao

原文摘要

Many everyday objects are difficult to directly grasp (e.g., a flat iPad) or manipulate functionally (e.g., opening the cap of a pen lying on a desk). Such tasks require sequential, asymmetric coordination between two arms, where one arm performs preparatory manipulation that enables the other's goal-directed action--for instance, pushing the iPad to the table's edge before picking it up, or lifting the pen body to allow the other hand to remove its cap. In this work, we introduce Collaborative Preparatory Manipulation, a class of bimanual manipulation tasks that demand understanding object semantics and geometry, anticipating spatial relationships, and planning long-horizon coordinated actions between the two arms. To tackle this challenge, we propose a visual affordance-based framework that first envisions the final goal-directed action and then guides one arm to perform a sequence of preparatory manipulations that facilitate the other arm's subsequent operation. This affordance-centric representation enables anticipatory inter-arm reasoning and coordination, generalizing effectively across various objects spanning diverse categories. Extensive experiments in both simulation and the real world demonstrate that our approach substantially improves task success rates and generalization compared to competitive baselines.

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:162a2c5e4014700c626c38d948cdbecbCVF · CVPR 2026

Style-GRPO: Semantic-Aware Preference Optimization for Image Style Transfer Guided by Reward Modeling

Zhao, Jianbin, Feng, Chaoran, Yu, Miao, Li, Yingtao, Tang, Zhenyu, Yu, Wangbo, Zhao, Yian, Li, Xiaomin 等 10 位作者

原文摘要

Recent progress in text-to-image generation has greatly advanced visual fidelity and creativity, but it has also imposed higher demands on prompt complexity--particularly in encoding intricate spatial relationships. In such cases, achieving satisfactory results often requires multiple sampling attempts.To address this challenge, we introduce a novel method that strengthens the spatial understanding of current image generation models. We first construct the **SpatialReward-Dataset** with over 80k preference pairs. Building on this dataset, we build **SpatialScore**, a reward model designed to evaluate the accuracy of spatial relationships in text-to-image generation, achieving performance that even surpasses leading proprietary models on spatial evaluation.% Through rigorous data curation and filtering, **SpatialScore** surpasses several leading vision-language models (VLMs) and existing reward models on spatial evaluation. We further demonstrate that this reward model effectively enables online reinforcement learning for the complex spatial generation. Extensive experiments across multiple benchmarks show that our specialized reward model yields significant and consistent gains in spatial understanding for image generation. All models and datasets will be released.

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:1b38baaa8bf333bc39ff12625dfab42cCVF · CVPR 2026

LoL: Longer than Longer, Scaling Video Generation to Hour

Cui, Justin, Wu, Jie, Li, Ming, Yang, Tao, Li, Xiaojie, Wang, Rui, Bai, Andrew, Ban, Yuanhao 等 9 位作者

原文摘要

Recent research in long-form video generation has shifted from bidirectional to autoregressive models, yet these methods commonly suffer from error accumulation and a loss of long-term coherence. While attention sink frames have been introduced to mitigate this performance decay, they often induce a critical failure mode we term sink-collapse: the generated content repeatedly reverts to the sink frame, resulting in abrupt scene resets and cyclic motion patterns. Our analysis reveals that sink-collapse originates from an inherent conflict between the periodic structure of Rotary Position Embedding (RoPE) and the multi-head attention mechanisms prevalent in current generative models. To address it, we propose a lightweight, training-free approach that effectively suppresses this behavior by introducing multi-head RoPE jitter that breaks inter-head attention homogenization and mitigates long-horizon collapse. Extensive experiments show that our method successfully alleviates sink-collapse while preserving generation quality. To the best of our knowledge, this work achieves the first demonstration of real-time, streaming, and infinite-length video generation with little quality decay. As an illustration of this robustness, we generate continuous videos up to 12 hours in length, which, to our knowledge, is among the longest publicly demonstrated results in streaming video generation.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:22f760450fd95ae42743ff5a75e5a449CVF · CVPR 2026

CubeComposer: Spatio-Temporal Autoregressive 4K 360deg Video Generation from Perspective Video

Li, Lingen, Wang, Guangzhi, Li, Xiaoyu, Zhang, Zhaoyang, Dou, Qi, Gu, Jinwei, Xue, Tianfan, Shan, Ying

原文摘要

Generating high-quality 360deg panoramic videos from perspective input is one of the crucial applications for virtual reality (VR), whereby high-resolution videos are especially important for immersive experience. Existing methods are constrained by computational limitations of vanilla diffusion models, only supporting <= 1K resolution native generation and relying on suboptimal post-hoc super-resolution. We introduce CubeComposer, a novel spatio-temporal autoregressive diffusion model that natively generates 4K-resolution 360deg videos. By decomposing videos into cubemap representations with six faces, CubeComposer autoregressively synthesizes content in a well-planned spatio-temporal order, reducing peak memory demands while enabling high-resolution output. Specifically, to address challenges in multi-dimensional autoregression, we propose: (1) a spatio-temporal autoregressive strategy that orchestrates 360deg video generation across cube faces and time windows for coherent synthesis; (2) a cube face context management mechanism, equipped with a sparse context attention design to improve efficiency; and (3) continuity-aware techniques, including cube-aware positional encoding, padding, and blending to eliminate boundary seams. Extensive experiments on benchmark datasets demonstrate that CubeComposer outperforms state-of-the-art methods in native resolution and visual quality, supporting practical VR application scenarios.

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:23173dd661fad72cdd0f640c89935163CVF · CVPR 2026

FVAR: Next-Focus Prediction for Visual Autoregressive Modeling

Li, Xiaofan, Wu, Chenming, Sun, Yanpeng, Zhou, Jiaming, Qu, Delin, Qu, Yansong, Bo, Weihao, Yu, Haibao 等 9 位作者

原文摘要

Visual autoregressive models achieve remarkable generation quality through next-scale predictions across multi-scale token pyramids. However, the conventional method uses uniform scale downsampling to build these pyramids, leading to aliasing artifacts that compromise fine details and introduce unwanted jaggies and moire patterns. To tackle this issue, we present FVAR, which reframes the paradigm from next-scale prediction to next-focus prediction, mimicking the natural process of camera focusing from blur to clarity. Our approach introduces three key innovations: 1) Next-Focus Prediction Paradigm that transforms multi-scale autoregression by progressively reducing blur rather than simply downsampling; 2) Progressive Refocusing Pyramid Construction that uses physics-consistent defocus kernels to build clean, alias-free multi-scale representations; and 3) High-Frequency Residual Learning that employs a specialized residual teacher network to effectively incorporate alias information during training while maintaining deployment simplicity. Specifically, we construct optical low-pass views using defocus point spread function (PSF) kernels with decreasing radius, creating smooth blur-to-clarity transitions that eliminate aliasing at its source. To further enhance detail generation, we introduce a High-Frequency Residual Teacher that learns from both clean structure and alias residuals, distilling this knowledge to a vanilla VAR deployment network for seamless inference. Extensive experiments on ImageNet demonstrate that FVAR substantially reduces aliasing artifacts, improves fine detail preservation, and enhances text readability, achieving superior performance with perfect compatibility to existing VAR frameworks.

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:28dca0acaf837ed5db138d8f2156203aCVF · CVPR 2026

FaithFusion: Harmonizing Reconstruction and Generation via Pixel-wise Information Gain

Wang, YuAn, Li, Xiaofan, Huang, Chi, Zhang, Wenhao, Li, Hao, Wang, Bosheng, Sun, Xun, Wang, Jun

原文摘要

In controllable driving-scene reconstruction and 3D scene generation, maintaining geometric fidelity while synthesizing visually plausible appearance under large viewpoint shifts is crucial. However, effective fusion of geometry-based 3DGS and appearance-driven diffusion models faces inherent challenges, as the absence of pixel-wise, 3D-consistent editing criteria often leads to over-restoration and geometric drift. To address these issues, we introduce **FaithFusion**, a 3DGS-diffusion fusion framework driven by pixel-wise Expected Information Gain (EIG). EIG acts as a unified policy for coherent spatio-temporal synthesis: it guides diffusion as a spatial prior to refine high-uncertainty regions, while its pixel-level weighting distills the edits back into 3DGS. The resulting plug-and-play system is free from extra prior conditions and structural modifications. Extensive experiments on the Waymo dataset demonstrate that our approach attains SOTA performance across NTA-IoU, NTL-IoU, and FID, maintaining an FID of 107.47 even at 6 meters lane shift.

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:2ade88afb0252af0f285d8b585e57faaCVF · CVPR 2026

Where Does Vision Meet Language? Understanding and Refining Visual Fusion in MLLMs via Contrastive Attention

Song, Shezheng, Li, Shasha, Zhao, Shan, Li, Xiaopeng, Wan, Qian, Wang, Chengyu, Yan, Tianwei, Jun, Ma 等 9 位作者

原文摘要

Multimodal Large Language Models (MLLMs) have achieved remarkable progress in vision-language understanding, yet how they internally integrate visual and textual information remains poorly understood. To bridge this gap, we perform a systematic layer-wise masking analysis across multiple architectures, revealing how visual-text fusion evolves within MLLMs. The results show that fusion emerges at several specific layers rather than being uniformly distributed across the network, and certain models exhibit a late-stage "review" phenomenon where visual signals are reactivated before output generation. Besides, we further analyze layer-wise attention evolution and observe persistent high-attention noise on irrelevant regions, along with gradually increasing attention on text-aligned areas. Guided by these insights, we introduce a training-free contrastive attention framework that models the transformation between early fusion and final layers to highlight meaningful attention shifts. Extensive experiments across various MLLMs and benchmarks validate our analysis and demonstrate that the proposed approach improves multimodal reasoning performance.Code will be released.

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:2af612063d1def181af331708ab66f52CVF · CVPR 2026

HiFICL: High-Fidelity In-Context Learning for Multimodal Tasks

Li, Xiaoyu, Liu, Yuhang, Kang, Xuanshuo, Luo, Zheng, Lou, Fangqi, Wu, Xiaohua, Xiong, Zihan

原文摘要

In-Context Learning (ICL) is a significant paradigm for Large Multimodal Models (LMMs), using a few in-context demonstrations (ICDs) for new task adaptation. However, its performance is sensitive to demonstration configurations and computationally expensive. Mathematically, the influence of these demonstrations can be decomposed into a dynamic mixture of the standard attention output and the context values. Current approximation methods simplify this process by learning a "shift vector". Inspired by the exact decomposition, we introduce High-Fidelity In-Context Learning (HiFICL) to more faithfully model the ICL mechanism. HiFICL consists of three key components: 1) a set of "virtual key-value pairs" to act as a learnable context, 2) a low-rank factorization for stable and regularized training, and 3) a simple end-to-end training objective. From another perspective, this mechanism constitutes a form of context-aware Parameter-Efficient Fine-Tuning (PEFT). Extensive experiments show that HiFICL consistently outperforms existing approximation methods on several multimodal benchmarks.

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: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:42b6f3a7fd74474d77239969becf6378CVF · CVPR 2026

From Exploration to Exploitation: A Two-Stage Entropy RLVR Approach for Noise-Tolerant MLLM Training

Xu, Donglai, Yang, Hongzheng, Zhao, Yuzhi, Zhang, Pingping, Chen, Jinpeng, Ma, Wenao, Hou, Zhijian, Wu, Mengyang 等 13 位作者

原文摘要

Reinforcement Learning with Verifiable Rewards (RLVR) for Multimodal Large Language Models (MLLMs) is highly dependent on high-quality labeled data, which is often scarce and prone to substantial annotation noise in real-world scenarios. Existing unsupervised RLVR methods, including pure entropy minimization, can overfit to incorrect labels and limit the crucial reward ranking signal for Group-Relative Policy Optimization (GRPO). To address these challenges and enhance noise tolerance, we propose a novel two-stage, token-level entropy optimization method for RLVR. This approach dynamically guides the model from exploration to exploitation during training. In the initial exploration phase, token-level entropy maximization promotes diverse and stochastic output generation, serving as a strong regularizer that prevents premature convergence to noisy labels and ensures sufficient intra-group variation--enabling more reliable reward gradient estimation in GRPO. As training progresses, the method transitions into the exploitation phase, where token-level entropy minimization encourages the model to produce confident and deterministic outputs, thereby consolidating acquired knowledge and refining prediction accuracy. Empirically, across three MLLM backbones-Qwen2-VL-2B, Qwen2-VL-7B, and Qwen2.5-VL-3B--spanning diverse noise settings and multiple tasks, our phased strategy consistently outperforms prior approaches by unifying and enhancing external, internal, and entropy-based methods, delivering robust and superior performance across the board.

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:467632e7acdbef45d6afb63c5a1e28b9CVF · CVPR 2026

Taming Noise-Induced Prototype Degradation for Privacy-Preserving Personalized Federated Fine-Tuning

Wang, Yuhua, Zhang, Qinnan, Li, Xiaodong, Zhang, Huan, Sun, Yifan, Qiu, Wangjie, Zhang, Hainan, Tong, Yongxin 等 9 位作者

原文摘要

Prototype-based Personalized Federated Learning (ProtoPFL) enables efficient multi-domain adaptation by communicating compact class prototypes, but directly sharing them poses privacy risks. A common defense involves per-example l_2 clipping before prototype computation to bound sensitivity, followed by isotropic Gaussian noise to enforce Local Differential Privacy (LDP). However, Isotropic Gaussian Prototype Perturbation (IGPP) typically over-perturbs discriminative dimensions and struggles to balance the clipping threshold with representation fidelity. In this paper, we propose VPDR, a client-side privacy plug-in that seamlessly integrates into existing ProtoPFLs. Motivated by the observation that dimension-wise class variance reflects discriminability, we introduce Variance-adaptive Prototype Perturbation (VPP), which allocates less noise to discriminative subspaces, preserving semantic separability while ensuring privacy. We further develop Distillation-guided Clipping Regularization (DCR), which enables feature norms to adaptively concentrate near the predefined clipping threshold while maintaining prediction consistency. Theoretical analysis shows that our groupwise mechanism provides privacy guarantees no weaker than the isotropic baseline under the same privacy constraints. Extensive experiments on multi-domain benchmarks demonstrate that VPDR achieves a superior privacy-utility trade-off, outperforming IGPP in personalized federated fine-tuning without sacrificing robustness against realistic attacks.

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:51fa911d816fef9b08d906414ad436b2CVF · CVPR 2026

Ov3R: Open-Vocabulary Semantic 3D Reconstruction from RGB Videos

Gong, Ziren, Li, Xiaohan, Tosi, Fabio, Han, Jiawei, Mattoccia, Stefano, Cai, Jianfei, Poggi, Matteo

原文摘要

We present Ov3R, a novel framework for open-vocabulary semantic 3D reconstruction from RGB video streams, designed to advance Spatial AI. The system features two key components: CLIP3R, a CLIP-informed 3D reconstruction module that predicts dense point maps from overlapping clips alongside object-level semantics; and 2D-3D OVS, a 2D-3D open-vocabulary semantic module that lifts 2D features into 3D by learning fused descriptors integrating spatial, geometric, and semantic cues. Unlike prior methods, Ov3R incorporates CLIP semantics directly into the reconstruction process, enabling globally consistent geometry and fine-grained semantic alignment. Our framework achieves state-of-the-art performance in both dense 3D reconstruction and open-vocabulary 3D segmentation.

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:5d92db16f4f1455f18e9077496ff2084CVF · CVPR 2026

TLMA: Mitigating the Impact of Weakly Labeled Information for Video Anomaly Detection

Xu, Rong, Wang, Runqi, Zhang, Yingjun, Tao, Tao, Li, Xiaomeng, Jing, Liping

原文摘要

Weakly Supervised Video Anomaly Detection (WSVAD) aims to localize abnormal segments using only video-level labels during training.Although the paradigm significantly reduces annotation costs, the coarse-grained labels fail to precisely describe the full videos, resulting in the introduction of substantial Weakly Labeled Information (WLI) during training. The presence of WLI makes it difficult for the model to accurately learn the boundary between normal and abnormal behaviors, leading to misclassifications and compromising the precision of anomaly localization.To tackle the challenges posed by WLI, we propose a triplet learning strategy that selects hard segments from normal videos as anchors. By combining contrastive learning with Multiple Instance Learning (MIL) strategy, we increase the projection distance between abnormal segments and anchor samples, to reduce the interference of WLI in anomaly detection.Moreover, considering that anomalies typically occur in dynamic foreground regions, we further design a motion-aware feature enhancement module that extracts dynamic areas within each video segment to emphasize the representation of critical features.This not only improves the accuracy of anchors in triplets, but also enhances the discriminative power of instance features in MIL. Extensive experiments on UCF-Crime, XD-Violence, and MSAD datasets demonstrate the effectiveness of our approach.

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:6598b405244e63adddc955dee788b954CVF · CVPR 2026

Enhancing Descriptive Captions with Visual Attributes for Multimodal Perception

Sun, Yanpeng, Hao, Jing, Zhu, Ke, Liu, Jiang-Jiang, Li, Xiaofan, Zhao, Na, Li, Zechao, Wang, Jingdong

原文摘要

Training Large Multimodality Models (LMMs) relies on descriptive image caption that connects image and language. Existing methods for generating such captions often rely on distilling the captions from pretrained LMMs, constructing them from publicly available internet images, or even generating them through human annotation. However, these strategies can fall short in terms of precision and granularity, particularly when dealing with complex visual reasoning tasks. In this paper, we propose to leverage off-the-shelf visual specialists, which were trained from annotated images initially not for image captioning, for enhancing the image caption. Our approach, named Cap-Workflow, explores object low-level and fine-grained attributes (e.g., depth, emotion and fine-grained categories) and object relations (e.g., relative location and human-object-interaction (HOI)), and combine the attributes into the descriptive caption. By systematically integrating these rich attributes into the generated captions, Cap-Workflow significantly improves the descriptive quality of the captions, providing a deeper and more nuanced understanding of the visual content. Experiments demonstrate that such visual specialists are able to improve the performance for visual understanding tasks as well as reasoning that benefits from more accurate visual understanding. The source code and datasets are available at https://github.com/syp2ysy/Cap-Workflow.

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:6d2e8a2fc1199600f14c65c0db38a770CVF · CVPR 2026

From Manuals to Actions: A Unified VLA Model for Chain-of-Thought Manual Generation and Robotic Manipulation

Gu, Chenyang, Liu, Jiaming, Chen, Hao, Huang, Runzhong, Wuwu, Qingpo, Li, Xiaoqi, Liu, Zhuoyang, Li, Ying 等 12 位作者

原文摘要

Vision-Language-Action (VLA) models have recently emerged, demonstrating strong generalization in robotic scene understanding and manipulation. However, when confronted with long-horizon tasks that require defined goal states, such as LEGO assembly or object rearrangement, existing VLA models still face challenges in coordinating long-horizon planning with precise manipulation.Therefore, we aim to endow a VLA model with the capability to infer the "how" process from the "what" outcomes, transforming goal states into executable procedures. In this paper, we introduce ManualVLA, a unified VLA framework built upon a Mixture-of-Transformers (MoT) architecture, enabling coherent collaboration between multimodal manual generation and action execution. Unlike prior VLA models that directly map sensory inputs to actions, we first equip ManualVLA with a planning expert that generates intermediate manuals consisting of images, visual prompts, and textual instructions. Building upon these multimodal manuals, we design a Manual Chain-of-Thought (ManualCoT) reasoning process that feeds them into the action expert, where each manual step provides explicit control conditions, while its latent representation offers implicit guidance for accurate manipulation. To alleviate the burden of data collection, we develop a high-fidelity digital-twin toolkit based on 3D Gaussian Splatting, which automatically generates manual data for planning expert training. ManualVLA demonstrates strong real-world performance, achieving an average success rate 32% higher than the previous hierarchical SOTA baseline on LEGO assembly and object rearrangement tasks.

cs.CV

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

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FlashVSR: Towards Real-time Diffusion-Based Streaming Video Super Resolution

Zhuang, Junhao, Guo, Shi, Cai, Xin, Li, Xiaohui, Liu, Yihao, Yuan, Chun, Xue, Tianfan

原文摘要

Diffusion models have recently advanced video restoration, but applying them to real-world and AIGC-generated video super-resolution (VSR) remains challenging due to high latency, prohibitive computation, and poor generalization to ultra-high resolutions. Our goal in this work is to make diffusion-based VSR practical by achieving efficiency, scalability, and near real-time performance. To this end, we propose FlashVSR, the first diffusion-based one-step streaming framework for efficient video super-resolution. FlashVSR runs at 17 FPS for 768x1408 videos on a single A100 GPU by combining three complementary innovations: (i) a train-friendly three-stage distillation pipeline that enables streaming super-resolution, (ii) locality-constrained sparse attention that reduces redundant computation while bridging the train--test resolution gap, and (iii) a tiny conditional decoder that accelerates reconstruction without sacrificing quality. To support large-scale training, we also construct VSR-120K, a new dataset containing 120 K videos and 180 K images. Extensive experiments demonstrate that FlashVSR scales reliably to ultra-high resolutions and achieves state-of-the-art performance with up to 12x speed-up over prior one-step diffusion-based VSR models. We will release the code, pretrained models, and dataset to foster future research in efficient diffusion-based video super-resolution.

cs.CV

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

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InfinityHuman: Towards Long-Term Audio-Driven Human Animation

Li, Xiaodi, Xie, Pan, Ren, Yi, Gan, Qijun, Zhang, Chen, Kong, Fangyuan, Yin, Xiang, Yuan, Zehuan 等 9 位作者

原文摘要

Audio-driven human animation has attracted wide attention thanks to its practical applications. However, critical challenges remain in generating high-resolution, long-duration videos with consistent appearance and natural hand motions. Existing methods extend videos using overlapping motion frames but suffer from error accumulation, leading to identity drift, color shifts, and scene instability. Additionally, hand movements are poorly modeled, resulting in noticeable distortions and misalignment with the audio. In this work, we propose InfinityHuman, a coarse-to-fine framework that first generates audio-synchronized representations, then progressively refines them into high-resolution, long-duration videos using a pose-guided refiner. Since pose sequences are decoupled from appearance and resist temporal degradation, our pose-guided refiner employs stable poses and the initial frame as a visual anchor to reduce drift and improve lip synchronization. Moreover, to enhance semantic accuracy and gesture realism, we introduce a hand-specific reward mechanism trained with high-quality hand motion data. Experiments on the EMTD and HDTF datasets show that InfinityHuman achieves state-of-the-art performance in video quality, identity preservation, hand accuracy, and lip-sync. Ablation studies further confirm the effectiveness of each module. And our project page is available at https://infinityhuman.github.io/.

cs.CV

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

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EE-RL: Vision Language Guided Reinforcement Learning with Explorer and Expert model for End-to-End Autonomous Driving

Li, Xiaolong, Yang, Lan, Li, Ruyang, Fang, Shan, Liu, Yang, Zhao, Xiangmo

原文摘要

End-to-end driving frameworks, which directly map raw sensor data to vehicle control commands, have shown remarkable potential. However, their performance often deteriorates in sparse-critical scenarios, where rare but safety-sensitive events occur. To address this problem, we propose Explorer-Expert Reinforcement Learning (EE-RL), a novel end-to-end framework that integrates an RL-based explorer, a fine-tuned vision-language model (VLM)-based expert, and a dual replay buffer. EE-RL adopts a collaborative learning strategy in which an explorer and two experts jointly generate experiences from regular driving scenarios to guide policy learning. As training progresses, one of the VLMs focuses on reasoning about sparse-critical scenarios, thereby enhancing learning efficiency and policy optimization in both scenarios. Additionally, the StateHash algorithm is designed to separately measure RGB image similarity and vehicle kinematic similarity, thereby skipping unnecessary VLM reasoning and enabling denser, more effective expert experience generation. Experiments on the CARLA Leaderboard demonstrate that EE-RL significantly outperforms state-of-the-art (SOTA) baselines, achieving +19.82% and +20.98% improvements in driving and infraction scores on Town03, respectively. The EE-RL further achieves 0% accident probability for red-light running and an average driving score of 80.09 in Town05-06, demonstrating robustness, generalizability, and the capability to handle sparse-critical scenarios.

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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Deeper Thought, Weaker Aim: Understanding and Mitigating Perceptual Impairment during Reasoning in Multimodal Large Language Models

Peng, Ruiying, Wu, Xueyu, Lei, Jing, Hou, Lu, Ma, Yuanzheng, Li, Xiao-Hui

原文摘要

Multimodal large language models (MLLMs) often suffer from perceptual impairments under extended reasoning modes, particularly in visual question answering (VQA) tasks. We identify attention dispersion as the underlying cause: during multi-step reasoning, model's visual attention becomes scattered and drifts away from question-relevant regions, effectively "losing focus" on the visual input. To better understand this phenomenon, we analyze the attention maps of MLLMs and observe that reasoning prompts significantly reduce attention to regions critical for answering the question. We further find a strong correlation between model's overall attention on image tokens and the spatial dispersiveness of model's attention within the image. Leveraging this insight, we propose a training-free Visual Region-Guided Attention (VRGA) framework that selects visual heads based on an entropy-focus criterion and reweights their attention, effectively guiding the model to focus on question-relevant regions during reasoning. Extensive experiments on vision-language benchmarks demonstrate that our method effectively alleviates perceptual degradation, leading to improvements in visual grounding and reasoning accuracy, while offering interpretable insights into how MLLMs process visual information.

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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Hierarchical Long Video Understanding with Audiovisual Entity Cohesion and Agentic Search

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

原文摘要

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

cs.CV · cs.CL · cs.AI

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

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FM-Steer: Enhance Generalist Policies with Value-Guided Cascaded Denoising

Song, Haoming, Qu, Delin, Yao, Yuanqi, Chen, Qizhi, Li, Jiarui, Lv, Qi, Tang, Yiwen, Kang, Li 等 18 位作者

原文摘要

Humans naturally allocate more time before acting when handling complex tasks in the physical world. This paradigm has recently led to remarkable advances in boosting Large Language Models (LLMs) on complex tasks in digital domains. However, the potential of test-time computing remains largely unexplored for robotic foundation models that interact with the physical world. In this work, we propose FM-Steer, a test-time computing framework that augments flow-based Vision-Language-Action (VLA) generalist policies with value-guided sampling and cascaded action denoising, enabling stronger control performance and real-time action rates for dexterous robot manipulation. FM-Steer first introduces an intermediate flow verifier to estimate state-action values for candidate actions. At test time, the policy iteratively samples multiple noisy action proposals and retains the one with the highest predicted value, yielding value-aligned, high-quality actions without retraining. To satisfy the stringent frequency demands of robot control, FM-Steer further introduces cascaded action denoising, decoupling expensive value-guided sampling from fast action refinement. A lightweight Lite-Flow denoiser asynchronously takes the selected high-value noisy action and rapidly denoises it to produce the final control signal, enabling fluid, high-rate execution. During deployment, the intermediate verifier operates at a low frequency to provide value-guided sampling, while the Lite-Flow denoiser continually processes selected candidates to maintain real-time control. Extensive experiments demonstrate that FM-Steer scales flow-based VLA models effectively at test time and achieves state-of-the-art performance across diverse simulation benchmarks and real-world dexterous robotic tasks. The source code is available on the project page: https://hume-vla.github.io.

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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Real2Edit2Real: Generating Robotic Demonstrations via a 3D Control Interface

Zhao, Yujie, Fan, Hongwei, Chen, Di, Chen, Shengcong, Chen, Liliang, Li, Xiaoqi, Ren, Guanghui, Dong, Hao

原文摘要

Recent progress in robot learning has been driven by large-scale datasets and powerful visuomotor policy architectures, yet policy robustness remains limited by the substantial cost of collecting diverse demonstrations, particularly for spatial generalization in manipulation tasks. To reduce repetitive data collection, we present Real2Edit2Real, a framework that generates new demonstrations by bridging 3D editability with 2D visual data through a 3D control interface. Our approach first reconstructs scene geometry from multi-view RGB observations with a metric-scale 3D reconstruction model. Based on the reconstructed geometry, we perform depth-reliable 3D editing on point clouds to generate new manipulation trajectories while geometrically correcting the robot poses to recover physically consistent depth, which serves as a reliable condition for synthesizing new demonstrations. Finally, we propose a multi-conditional video generation model guided by depth as the primary control signal, together with action, edge, and ray maps, to synthesize spatially augmented multi-view manipulation videos. Experiments on four real-world manipulation tasks demonstrate that policies trained on data generated from only 1-5 source demonstrations can match or outperform those trained on 50 real-world demonstrations, improving data efficiency by up to 10-50x. Moreover, experimental results on height and texture editing demonstrate the framework's flexibility and extensibility, indicating its potential to serve as a unified data generation framework.

cs.CV

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

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When Numbers Speak: Aligning Textual Numerals and Visual Instances in Text-to-Video Diffusion Models

Sun, Zhengyang, Chen, Yu, Zhou, Xin, Li, Xiaofan, Chen, Xiwu, Liang, Dingkang, Bai, Xiang

原文摘要

Text-to-video diffusion models have enabled open-ended video synthesis, but often struggle with generating the correct number of objects specified in a prompt. We introduce NUMINA, a training-free identify-then-guide framework for improved numerical alignment. NUMINA identifies prompt-layout inconsistencies by selecting discriminative self- and cross-attention heads to derive a countable latent layout. It then refines this layout conservatively and modulates cross-attention to guide regeneration. On the introduced CountBench, NUMINA improves counting accuracy by up to 7.4% on Wan2.1-1.3B, and by 4.9% and 5.5% on 5B and 14B models, respectively. Furthermore, CLIP alignment is improved while maintaining temporal consistency. These results demonstrate that structural guidance complements seed search and prompt enhancement, offering a practical path toward count-accurate text-to-video diffusion. The code is available at https://github.com/H-EmbodVis/NUMINA.

cs.CV

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

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VerseCrafter: Dynamic Realistic Video World Model with 4D Geometric Control

Zheng, Sixiao, Yin, Minghao, Hu, Wenbo, Li, Xiaoyu, Shan, Ying, Fu, Yanwei

原文摘要

Video world models aim to simulate dynamic, real-world environments, yet existing methods struggle to provide unified and precise control over camera and multi-object motion, as videos inherently capture dynamics in the projected 2D image plane. To bridge this gap, we introduce VerseCrafter, a geometry-driven video world model that generates dynamic, realistic videos from a unified 4D geometric world state. Our approach is centered on a novel 4D Geometric Control representation, which encodes the world state as a static background point cloud and per-object 3D Gaussian trajectories. This representation captures each object's motion path and probabilistic 3D occupancy over time, providing a flexible, category-agnostic alternative to rigid bounding boxes and parametric models. We render 4D Geometric Control into 4D control maps for a pretrained video diffusion model, enabling high-fidelity, view-consistent video generation that faithfully follows the specified dynamics. To enable training at scale, we develop an automatic data engine and construct VerseControl4D, a real-world dataset of 35K training samples with automatically derived prompts and rendered 4D control maps. Extensive experiments show that VerseCrafter achieves superior visual quality and more accurate control over camera and multi-object motion than prior methods.

cs.CV

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

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FedRE: A Representation Entanglement Framework for Model-Heterogeneous Federated Learning

Yao, Yuan, Wang, Lixu, Wu, Jiaqi, Song, Jin, Chen, Simin, Wang, Zehua, Tian, Zijian, Chen, Wei 等 10 位作者

原文摘要

Federated learning (FL) enables collaborative training across clients while preserving privacy. While most existing FL methods assume homogeneous model architectures, client heterogeneity in both data and resources makes this assumption impractical, thus motivating model-heterogeneous FL. To address this problem, we propose Federated Representation Entanglement (FedRE), a framework built upon a novel form of client knowledge termed entangled representation. Specifically, each client aggregates its local representations into a single entangled representation using normalized random weights, and then applies the same weights to integrate the corresponding one-hot label encodings into an entangled-label encoding. Both are subsequently uploaded to the server to train a global classifier. During training, each entangled representation is supervised across categories via its entangled-label encoding, while random weights are re-sampled at each round to introduce diversity, alleviating overconfidence in the global classifier and yielding smoother decision boundaries. Moreover, each client uploads a single entangled representation along with its entangled-label encoding, mitigating the risk of representation inversion attacks and reducing communication overhead. Extensive experiments demonstrate that FedRE achieves an effective trade-off among model performance, privacy protection, and communication overhead. The codes are available at https://github.com/AIResearch-Group/FedRE.

cs.CV

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

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ViLoMem: Agentic Learner with Grow-and-Refine Multimodal Semantic Memory

Bo, Weihao, Zhang, Shan, Sun, Yanpeng, Wu, Jingjing, Xie, Qunyi, Tan, Xiao, Chen, Kunbin, He, Wei 等 12 位作者

原文摘要

MLLMs exhibit strong reasoning on isolated queries, yet they operate de novo--solving each problem independently and often repeating the same mistakes. Existing memory-augmented agents mainly store past trajectories for reuse. However, trajectory-based memory suffers from brevity bias, gradually losing essential domain knowledge. More critically, even in truly multimodal problem-solving settings, it records only a **single-modality** trace of past behavior, failing to preserve how visual attention and logical reasoning jointly contributed to the solution. This is fundamentally misaligned with human cognition: semantic memory is both **multimodal and integrated**, preserving visual and abstract knowledge through coordinated but distinct representational streams. We thus introduce **ViLoMem**, a dual-stream memory framework that constructs compact, schema-based memory. It separately encodes visual distraction patterns and logical reasoning errors, enabling MLLMs to learn from their successful and failed experiences. Following a grow-and-refine principle, the system incrementally accumulates and updates multimodal semantic knowledge--preserving stable, generalizable strategies while avoiding catastrophic forgetting. Across nine multimodal benchmarks, **ViLoMem** consistently improves pass@1 accuracy and substantially reduces repeated visual and logical errors. Ablations confirm the necessity of dual-stream memory with explicit distraction-hallucination separation, demonstrating the value of error-aware multimodal memory for lifelong and cross-domain agentic learning.

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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Active Inference for Micro-Gesture Recognition: EFE-Guided Temporal Sampling and Adaptive Learning

Feng, Weijia, Yang, Jingyu, Zhang, Ruojia, Sun, Fengtao, Gao, Qian, Wang, Chenyang, Su, Tongtong, Guo, Jia 等 10 位作者

原文摘要

Micro-gestures are subtle and transient movements triggered by unconscious neural and emotional activities, holding great potential for human-computer interaction and clinical monitoring. However, their low amplitude, short duration, and strong inter-subject variability make existing deep models prone to degradation under low-sample, noisy, and cross-subject conditions. This paper presents an active inference-based framework for micro-gesture recognition, featuring Expected Free Energy (EFE)-guided temporal sampling and uncertainty-aware adaptive learning. The model actively selects the most discriminative temporal segments under EFE guidance, enabling dynamic observation and information gain maximization. Meanwhile, sample weighting driven by predictive uncertainty mitigates the effects of label noise and distribution shift. Experiments on the SMG dataset demonstrate the effectiveness of the proposed method, achieving consistent improvements across multiple mainstream backbones. Ablation studies confirm that both the EFE-guided observation and the adaptive learning mechanism are crucial to the performance gains. This work offers an interpretable and scalable paradigm for temporal behavior modeling under low-resource and noisy conditions, with broad applicability to wearable sensing, HCI, and clinical emotion monitoring.

cs.CV

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

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