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首次观测 · 记录 #1CVF · CVPR 2026:cvf:0609ebbcfbb5bcf90d18daf5caaf41eeCVF · CVPR 2026

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

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

原文摘要

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

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:16ecb0dd4b9649b0751219b8a6d4dc5dCVF · CVPR 2026

Subspace Alignment for CLIP-based Continual Learning via Canonical Correlation Analysis

Zhang, Huan, Dong, Shuyu, Zheng, Yujin, Wang, Dingwen, Fan, Shenghua, Lyu, Fan

原文摘要

Recent advances in CLIP-based continual learning have shown the potential of leveraging pre-trained vision-language models for sequential tasks. However, existing methods overlook a key problem we call Asymmetric Drift. In unimodal CLIP-based continual learning, the visual branch undergoes stronger adaptation because the visual distribution shifts significantly, whereas the text branch remains relatively stable due to the low variance of textual prompts. This imbalance increases the modality distance and degrades cross-modal alignment over time. To address this issue, we propose CCA-CL, a framework that accumulates visual-textual covariance statistics across tasks and solves Canonical Correlation Analysis to compute a shared subspace. In this subspace, the distance between visual and textual features is minimized, enabling better alignment without modifying CLIP parameters. This also makes our method naturally compatible with exemplar-free CL settings. To further capture nonlinear relationships that linear Canonical Correlation Analysis is hard to model, we introduce Random Fourier Projection as an extension. Experimental results demonstrate that CCA-CL effectively mitigates the asymmetric drift problem and achieves state-of-the-art performance on several 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:210fe47a7deed4c9623f1ff41e8fb70cCVF · CVPR 2026

Saliency-Guided Representation with Consistency Policy Learning for Visual Unsupervised Reinforcement Learning

Sun, Jingbo, Zhang, Qichao, Tu, Songjun, Fang, Xing, Zheng, Yupeng, Li, Haoran, Chen, Ke, Zhao, Dongbin

原文摘要

Zero-shot unsupervised reinforcement learning (URL) offers a promising direction for building generalist agents capable of generalizing to unseen tasks without additional supervision. Among existing approaches, successor representations (SR) have emerged as a prominent paradigm due to their effectiveness in structured, low-dimensional settings. However, SR methods struggle to scale to high-dimensional visual environments. Through empirical analysis, we identify two key limitations of SR in visual URL: (1) SR objectives often lead to suboptimal representations that attend to dynamics-irrelevant regions, resulting in inaccurate successor measures and degraded task generalization; and (2) these flawed representations hinder SR policies from modeling multi-modal skill-conditioned action distributions and ensuring skill controllability. To address these limitations, we propose Saliency-Guided Representation with Consistency Policy Learning (SRCP), a novel framework that improves zero-shot generalization of SR methods in visual URL. SRCP decouples representation learning from successor training by introducing a saliency-guided dynamics task to capture dynamics-relevant representations, thereby improving successor measure and task generalization. Moreover, it integrates a fast-sampling consistency policy with URL-specific classifier-free guidance and tailored training objectives to improve skill-conditioned policy modeling and controllability. Extensive experiments on 16 tasks across 4 datasets from the ExORL benchmark demonstrate that SRCP achieves state-of-the-art zero-shot generalization in visual URL and is compatible with various SR methods.

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:42cef98d516c70ce073b35aeb0ee208bCVF · CVPR 2026

ShowTable: Unlocking Creative Table Visualization with Collaborative Reflection and Refinement

Liu, Zhihang, Bao, Xiaoyi, Li, Pandeng, Zhou, Junjie, Liao, Zhaohe, He, Yefei, Jiang, Kaixun, Xie, Chen-Wei 等 10 位作者

原文摘要

While existing generation and unified models excel at general image generation, they struggle with tasks requiring deep reasoning, planning, and precise data-to-visual mapping abilities beyond general scenarios. To push beyond the existing limitations, we introduce a new and challenging task: creative table visualization, requiring the model to generate an infographic that faithfully and aesthetically visualizes the data from a given table. To address this challenge, we propose ShowTable, a pipeline that synergizes MLLMs with diffusion models via a progressive self-correcting process. The MLLM acts as the central orchestrator for reasoning the visual plan and judging visual errors to provide refined instructions, the diffusion execute the commands from MLLM, achieving high-fidelity results. To support this task and our pipeline, we introduce three automated data construction pipelines for training different modules. Furthermore, we introduce TableVisBench, a new benchmark with 800 challenging instances across 5 evaluation dimensions, to assess performance on this task. Experiments demonstrate that our pipeline, instantiated with different models, significantly outperforms baselines, highlighting its effective multi-modal reasoning, generation, and error correction 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:4a3ae440946797153407236f4d5ab19cCVF · CVPR 2026

Self-Attention Driven Tensor Representation for High-Order Data Recovery

Shi, Zhi-Wei, Zheng, Yu-Bang, Li, Heng-Chao

原文摘要

Low-rank tensor representation (LRTR) is an effective tool for compactly modeling high-order data. While nonlinear LRTR models can better capture real-world nonlinear dependencies, most existing methods rely on fixed local mappings of multilayer perceptrons (MLPs) or convolutional neural networks (CNNs), limiting their ability to model complex global dependencies. To overcome this limitation, we construct a novel paradigm called Self-Attention Driven Tensor Representation (SADTR), which is the first framework that models nonlinearity from the perspective of self-attention. Specifically, we design a factor self-representation mechanism to establish dynamic global mapping, thereby adaptively capturing both local and non-local nonlinear dependencies in the factor space. Moreover, we introduce an implicit sparse representation to impose sparsity constraint while avoiding additional optimization problems. As a result, the proposed SADTR can achieve a more accurate low-rank representation. In theory, we provide a detailed analysis to demonstrate the recoverability of SADTR. To validate the effectiveness of SADTR, we apply it to three representative high-order data recovery tasks. Experimental results demonstrate that SADTR consistently outperforms existing state-of-the-art LRTR methods.

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:51a5dc1ea2dfc8f13628818ee7b88411CVF · CVPR 2026

Seeing Beyond: Extrapolative Domain Adaptive Panoramic Segmentation

Zheng, Yuanfan, Peng, Kunyu, Zheng, Xu, Yang, Kailun

原文摘要

Cross-domain panoramic semantic segmentation has attracted growing interest as it enables comprehensive 360^ \circ scene understanding for real-world applications. However, it remains particularly challenging due to severe geometric Field of View (FoV) distortions and inconsistent open-set semantics across domains. In this work, we formulate an open-set domain adaptation setting, and propose Extrapolative Domain Adaptive Panoramic Segmentation (EDA-PSeg) framework that trains on local perspective views and tests on full 360^ \circ panoramic images, explicitly tackling both geometric FoV shifts across domains and semantic uncertainty arising from previously unseen classes. To this end, we propose the Euler-Margin Attention (EMA), which introduces an angular margin to enhance viewpoint-invariant semantic representation, while performing amplitude and phase modulation to improve generalization toward unseen classes. Additionally, we design the Graph Matching Adapter (GMA), which builds high-order graph relations to align shared semantics across FoV shifts while effectively separating novel categories through structural adaptation. Extensive experiments on four benchmark datasets under camera-shift, weather-condition, and open-set scenarios demonstrate that EDA-PSeg achieves state-of-the-art performance, robust generalization to diverse viewing geometries, and resilience under varying environmental conditions. The code is available at https://github.com/zyfone/EDA-PSeg.

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:554d2845d26759c4aac26e6dc57824d6CVF · CVPR 2026

Structural Graph Probing of Vision-Language Models

He, Haoyu, Zhuo, Yue, Zheng, Yu, Wang, Qi R.

原文摘要

Vision-language models (VLMs) achieve strong multimodal performance, yet how computation is organized across populations of neurons remains poorly understood. In this work, we study VLMs through the lens of neural topology, representing each layer as a within-layer correlation graph derived from neuron-neuron co-activations. This view allows us to ask whether population-level structure is behaviorally meaningful, how it changes across modalities and depth, and whether it identifies causally influential internal components under intervention. We show that correlation topology carries recoverable behavioral signal; moreover, cross-modal structure progressively consolidates with depth around a compact set of recurrent hub neurons, whose targeted perturbation materially alters model output. Neural topology thus emerges as a meaningful intermediate scale for VLM interpretability: richer than local attribution, more tractable than full circuit recovery, and empirically tied to multimodal behavior. Code is publicly available at https://github.com/he-h/vlm-graph-probing.

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:7bd706f9c15ed767e79ea85024363349CVF · CVPR 2026

GEM-TFL: Bridging Weak and Full Supervision for Forgery Localization through EM-Guided Decomposition and Temporal Refinement

Zhu, Xiaodong, Zheng, Yuanming, Wang, Suting, Yang, Junqi, Yang, Yuhong, Tu, Weiping, Wang, Zhongyuan

原文摘要

Temporal Forgery Localization (TFL) aims to precisely identify manipulated segments within videos or audio streams, providing interpretable evidence for multimedia forensics and security. While most existing TFL methods rely on dense frame-level labels in a fully supervised manner, Weakly Supervised TFL (WS-TFL) reduces labeling cost by learning only from binary video-level labels. However, current WS-TFL approaches suffer from mismatched training and inference objectives, limited supervision from binary labels, gradient blockage caused by non-differentiable top-k aggregation, and the absence of explicit modeling of inter-proposal relationships. To address these issues, we propose GEM-TFL (Graph-based EM-powered Temporal Forgery Localization), a two-phase classification-regression framework that effectively bridges the supervision gap between training and inference. Built upon this foundation, (1) we enhance weak supervision by reformulating binary labels into multi-dimensional latent attributes through an EM-based optimization process; (2) we introduce a training-free temporal consistency refinement that realigns frame-level predictions for smoother temporal dynamics; and (3) we design a graph-based proposal refinement module that models temporal-semantic relationships among proposals for globally consistent confidence estimation. Extensive experiments on benchmark datasets demonstrate that GEM-TFL achieves more accurate and robust temporal forgery localization, substantially narrowing the gap with fully supervised methods.

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:834505b1dcef9b7af5ff2528e3671df0CVF · CVPR 2026

Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning

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

原文摘要

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

cs.CV · cs.CL

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:a9bf2f320addbe10ef635b10c92a82d9CVF · CVPR 2026

DVAR: Dynamic Visual Autoregressive Modeling for Image Super-Resolution

Zheng, Yu, Zhang, Kai, Zhu, Wei, Liu, Qingguo, Hu, Xiantao, Li, Jun, Yang, Jian

原文摘要

Next-scale prediction paradigm visual autoregressive (VAR) models have demonstrated significant potential for image super-resolution. However, their practical application is constrained by a rigid, size-specific design. This limitation stems from their reliance on memorizing fixed, absolute scaling schedules, which necessitates a distinct model for each target resolution. We introduce DVAR, a Dynamic Visual AutoRegressive framework that overcomes this fundamental bottleneck. Instead of memorizing these rigid schedules, DVAR learns a canonical scaling dynamic. This dynamic effectively decouples the logic of relative scaling from the absolute target size, thereby preserving a single set of proportions between generative steps that can be applied uniformly to any size. Furthermore, we introduce a dynamic sampling scheduler to mitigate the teacher-forcing problem with negligible computational overhead. By leveraging the geometric proximity of visual tokens in the codebook, it efficiently simulates the model's predictive error distribution to bridge the training-inference gap. To our knowledge, DVAR is the first framework to grant VAR models size-flexibility, breaking their one-to-one dependency on a fixed resolution. Extensive evaluations demonstrate that DVAR achieves superior visual quality over existing Real-ISR methods, proving that a flexible, purely autoregressive approach is a viable path to state-of-the-art image super-resolution. Code is available at https://github.com/YuZheng9/DVAR.

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:abcccfbb19baeb1fe7772eab2fc9fedcCVF · CVPR 2026

Progressive Guessing to Fixed Point: Rethinking Human Motion Prediction with Deep Equilibrium Models

Wei, Dong, Sun, Huaijiang, Liu, Fan, Zheng, Yuhui

原文摘要

Many recent human motion prediction methods adopt a multi-stage refinement framework, where each stage produces an initial guess of future poses for the next stage. These guesses are progressively refined towards the target prediction through a sequence of spatial-temporal reasoning stages.However, such a cascaded design incurs large computation and memory overheads that grow at least linearly with network depth, and lack an explicit stopping criteria.In this paper, we propose MotionDEQ, a deep equilibrium motion predictor that reformulates progressive guessing paradigm as a fixed point problem within an implicit layer. This formulation is conceptually equivalent to performing infinitely many refinement steps, but requires only O(1) training memory and can be solved efficiently through any black-box solvers. We carefully design this implicit refinement process by integrating Euclidean geometric transformations into equilibrium learning, allowing the entire network to be equivariant. We also find DEQs naturally fit the real-world scenario where motion data comes streamingly: the converged fixed point can be reused as a warm initial guess, to help recycle the redundant inference computation when making subsequent predictions.Our experiments demonstrate that MotionDEQ achieves the state-of-the-art prediction performances with superior memory efficiency, using fewer than 300K parameters with 55.3mm prediction error at 400ms on the Human3.6M dataset.

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:b47b62e8578a1fe8930b00e21a336bd8CVF · CVPR 2026

Scaling Test-Time Robustness of Vision-Language Models via Self-Critical Inference Framework

Tang, Kaihua, Qi, Jiaxin, Ou, Jinli, Zheng, Yuhua, Huang, Jianqiang

原文摘要

The emergence of Large Language Models (LLMs) has driven rapid progress in multi-modal learning, particularly in the development of Large Vision-Language Models (LVLMs). However, existing LVLM training paradigms place excessive reliance on the LLM component, giving rise to two critical robustness challenges: language bias and language sensitivity. To address both issues simultaneously, we propose a novel Self-Critical Inference (SCI) framework that extends Visual Contrastive Decoding by conducting multi-round counterfactual reasoning through both textual and visual perturbations. This process further introduces a new strategy for improving robustness by scaling the number of counterfactual rounds. Moreover, we also observe that failure cases of LVLMs differ significantly across models, indicating that fixed robustness benchmarks may not be able to capture the true reliability of LVLMs. To this end, we propose the Dynamic Robustness Benchmark (DRBench), a model-specific evaluation framework targeting both language bias and sensitivity issues. Extensive experiments show that SCI consistently outperforms baseline methods on DRBench, and that increasing the number of inference rounds further boosts robustness beyond existing single-step counterfactual reasoning 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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e249d3e9cdd89edc953b21e15aa83226CVF · CVPR 2026

Incentivizing Versatile Video Reasoning in MLLMs via Data-Efficient Reinforcement Learning

Wang, Xiaodong, Wu, Zhirong, Huang, Langling, Zheng, Yuxi, Peng, Peixi

原文摘要

Multimodal Large Language Models (MLLMs) have made great progress in video understanding tasks. However, when it comes to understanding complex or lengthy videos, MLLMs tend to overlook details or produce hallucinations. To alleviate these issues, recent work has attempted to leverage reinforcement learning (RL) to boost models' deep linguistic reasoning of complex videos. But these methods have two main problems: First, the RL framework they used has unstable training, high training costs, and is difficult to train satisfactory video reasoning models; Second, the linguistic reasoning process is difficult to guarantee the reliability of visual information. To alleviate these problems, we propose to use multimodal elements for reasoning, and we design a novel framework to build and enhance versatile video reasoning capabilities on MLLMs. We carefully design a multi-task cold start and multi-task reinforcement learning to improve the model's visual perception and proficiency in multiple capabilities. In the inference phase, we leverage multimodal reasoning and dynamic sampling to further improve the performance. We verified the efficiency of the framework on a base MLLM (Qwen2-VL-7B-Base). Through cold-start with 3k data and reinforcement learning training with 5k data, combined with inference design, our final model significantly outperforms the base model on seven public video benchmarks, even surpassing and approaching the state-of-the-art Instruct Models such as Qwen2.5-VL-7B-Instruct trained with large-scale data. Our code will be available at https://github.com/Wang-Xiaodong1899/VideoReasoner.

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:f3707273d5d3608c4351a451be7a9d3dCVF · CVPR 2026

A3: Towards Advertising Aesthetic Assessment

Ji, Kaiyuan, Gao, Yixuan, Sun, Lu, Zheng, Yushuo, Chen, Zijian, Zhang, Jianbo, Zhu, Xiangyang, Tian, Yuan 等 10 位作者

原文摘要

Advertising images significantly impact commercial conversion rates and brand equity, yet current evaluation methods rely on subjective judgments, lacking scalability, standardized criteria, and interpretability. To address these challenges, we present **A3 (Advertising Aesthetic Assessment)**, a comprehensive framework encompassing four components: a paradigm (**A3-Law**), a dataset (**A3-Dataset**), a multimodal large language model (**A3-Align**), and a benchmark (**A3-Bench**). Central to A3 is a theory-driven paradigm, A3-Law, comprising three hierarchical stages: (1) Perceptual Attention, evaluating perceptual image signals for their ability to attract attention; (2) Formal Interest, assessing formal composition of image color and spatial layout in evoking interest; and (3) Desire Impact, measuring desire evocation from images and their persuasive impact. Building on A3-Law, we construct A3-Dataset with 120K instruction-response pairs from 30K advertising images, each richly annotated with multi-dimensional labels and Chain-of-Thought (CoT) rationales. We further develop A3-Align, trained under A3-Law with CoT-guided learning on A3-Dataset. Extensive experiments on A3-Bench demonstrate that A3-Align achieves superior alignment with A3-Law compared to existing models, and this alignment generalizes well to quality advertisement selection and prescriptive advertisement critique, indicating its potential for broader deployment. Dataset, code, and models can be found at: https://github.com/euleryuan/A3-Align

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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