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首次观测 · 记录 #1CVF · CVPR 2026:cvf:04140b11181e276de5d0f286e687f719CVF · CVPR 2026

Thinking with Video: Video Generation as a Promising Multimodal Reasoning Paradigm

Tong, Jingqi, Mou, Yurong, Li, Hangcheng, Li, Mingzhe, Yang, Yongzhuo, Zhang, Ming, Chen, Qiguang, Liang, Tianyi 等 14 位作者

原文摘要

The "Thinking with Text" and "Thinking with Images" paradigms significantly improve the reasoning abilities of large language models (LLMs) and Vision-Language Models (VLMs). However, these paradigms have inherent limitations. (1) Images capture only single moments and fail to represent dynamic processes or continuous changes, and (2) The separation of text and vision as distinct modalities, which hinders unified multimodal understanding and generation. Therefore, we propose "Thinking with Video", a new paradigm that leverages video generation models such as Sora-2 to use video frames as a unified medium for multimodal reasoning. To support this exploration, we developed the Video Thinking Benchmark (VideoThinkBench), which covers both vision-centric tasks (e.g., Eyeballing Puzzles) and text-centric tasks (e.g., GSM8K and MMMU). Our evaluation on VideoThinkBench establishes Sora-2 as a capable reasoner. On vision-centric tasks, Sora-2 is comparable to state-of-the-art (SOTA) VLMs, and even surpasses GPT-5 by 10% on eyeballing puzzles. On text-centric tasks, Sora-2 achieves 92% accuracy on MATH, and 69.2% accuracy on MMMU. Furthermore, we systematically analyze the source of these abilities. We also find that self-consistency and in-context learning can improve Sora-2's performance. In summary, our findings show that the video generation model is the potential unified multimodal understanding and generation model, positioning "Thinking with Video" as a potential unified multimodal reasoning paradigm.

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:0b02895f0f4a35b76b93537e99c7e200CVF · CVPR 2026

Towards Foundation Models for 3D Scene Understanding: Instance-Aware Self-Supervised Learning for Point Clouds

Yang, Bin, Abdelsamad, Mohamed, Zhang, Miao, Condurache, Alexandru Paul

原文摘要

Recent advances in self-supervised learning (SSL) for point clouds have substantially improved 3D scene understanding without human annotations. Existing approaches emphasize semantic awareness by enforcing feature consistency across augmented views or by masked scene modeling. However, the resulting representations transfer poorly to instance localization, and often require full finetuning for strong performance. Instance awareness is a fundamental component of 3D perception, thus bridging this gap is crucial for progressing toward true 3D foundation models that support all downstream tasks on 3D data. In this work, we introduce PointINS, an instance-oriented self-supervised framework that enriches point cloud representations through geometry-aware learning.PointINS employs an orthogonal offset branch to jointly learn high-level semantic understanding and geometric reasoning, yielding instance awareness. We identify two consistent properties essential for robust instance localization and formulate them as complementary regularization strategies, Offset Distribution Regularization (ODR), which aligns predicted offsets with empirically observed geometric priors, and Spatial Clustering Regularization (SCR), which enforces local coherence by regularizing offsets with pseudo-instance masks. Through extensive experiments across five datasets, PointINS achieves on average +3.5% mAP improvement for indoor instance segmentation and +4.1% PQ gain for outdoor panoptic segmentation, paving the way for scalable 3D foundation models.

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:0bc5eef7b0b955ea7be62244fd04635dCVF · CVPR 2026

ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for Composed Image Retrieval

Li, Zixu, Hu, Yupeng, Chen, Zhiwei, Zhang, Mingyu, Fu, Zhiheng, Nie, Liqiang

原文摘要

The Composed Image Retrieval (CIR) task provides a flexible retrieval paradigm via a reference image and modification text, but it heavily relies on expensive and error-prone triplet annotations. This paper systematically investigates the Noisy Triplet Correspondence (NTC) problem introduced by annotations. We find that NTC noise, particularly "hard noise" (i.e., the reference and target images are highly similar but the modification text is incorrect), poses a unique challenge to existing Noise Correspondence Learning (NCL) methods because it breaks the traditional "small loss hypothesis". We identify and elucidate three key, yet overlooked, challenges in the NTC task, namely (C1) Modality Suppression, (C2) Negative Anchor Deficiency, and (C3) Unlearning Backlash. To address these challenges, we propose a Cone-based robuSt noisE-unlearning comPositional network (ConeSep). Specifically, we first propose Geometric Fidelity Quantization, theoretically establishing and practically estimating a noise boundary to precisely locate noisy correspondence. Next, we introduce Negative Boundary Learning, which learns a "diagonal negative combination" for each query as its explicit semantic opposite-anchor in the embedding space. Finally, we design Boundary-based Targeted Unlearning, which models the noisy correction process as an optimal transport problem, elegantly avoiding Unlearning Backlash. Extensive experiments on benchmark datasets (FashionIQ and CIRR) demonstrate that ConeSep significantly outperforms current state-of-the-art methods, which fully demonstrates the effectiveness and robustness 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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:0f6665c67f7d5c95b45ccb3a8570f005CVF · CVPR 2026

QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models

Zhang, Jingxuan, Hsieh, Yunta, Wan, Zhongwei, Lin, Haokun, Wang, Xin, Wang, Ziqi, Lei, Yingtie, Zhang, Mi

原文摘要

Vision-language-action (VLA) models unify perception, language, and control for embodied agents but face significant challenges in practical deployment due to rapidly increasing compute and memory demands, especially as models scale to longer horizons and larger backbones. To address these bottlenecks, we introduce QuantVLA, a training-free post-training quantization (PTQ) framework that, to our knowledge, is the first PTQ approach for VLA systems and the first to successfully quantize a diffusion transformer (DiT) action head. QuantVLA incorporates three scale-calibrated components: (1) a selective quantization layout that integerizes all linear layers in both the language backbone and the DiT while keeping attention projections in floating point to preserve the original operator schedule; (2) attention temperature matching, a lightweight per-head scaling mechanism that stabilizes attention logits and is folded into the dequantization scales at inference; and (3) output head balancing, a per-layer residual interface calibration that mitigates post-projection energy drift. The framework requires no additional training, uses only a small unlabeled calibration buffer, and supports integer kernels for low-bit weights and activations while leaving the architecture unchanged. Across representative VLA models on LIBERO, QuantVLA exceeds the task success rates of full-precision baselines, achieves about 70% relative memory savings on the quantized components, providing a practical pathway toward scalable low-bit embodied intelligence under strict compute, memory, and power constraints.

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:1d8a1456763d09b50d9cfcc37c7ed264CVF · CVPR 2026

PhysIR-Splat: Physically Consistent Thermal Infrared Radiative Transfer in 3D Gaussian Splatting

Gao, Jingyuan, Hu, Yumeng, Gao, Fei, Zhang, Mingjin

原文摘要

Thermal infrared (TIR) 3D reconstruction provides geometry that is intrinsically coupled to the temperature field, even in low-light, nighttime, and smoke-obscured environments. TIR imaging measures self-emitted thermal radiation driven by object temperature and is largely independent of external illumination; therefore, simply carrying over visible-spectrum assumptions to TIR-based 3D reconstruction and novel view synthesis (NVS) often results in floating artifacts and blurred edges. In addition, radiometric inconsistency and low contrast in TIR weaken structure-from-motion (SfM) initialization, which in turn hinders subsequent 3D Gaussian Splatting (3DGS) optimization. We present PhysIR-Splat, a 3DGS framework that follows infrared radiative transfer: we explicitly model temperature, emissivity, and environmental irradiance on Gaussian primitives and, during rendering, jointly account for thermal emission, the reflected component, and atmospheric transmittance to produce physically consistent thermal synthesis. We also introduce VGGT-IR, a Transformer-based feed-forward initializer that takes TIR input with optional RGB and directly regresses camera poses and initial geometry, providing a modality-aligned and stable starting point for PhysIR-Splat. Extensive experiments demonstrate that our method significantly surpasses existing approaches in thermal reconstruction quality and cross-view consistency, effectively suppressing floating artifacts and enhancing boundary sharpness. The source code will be publicly available at : https://github.com/JingyuanGao0919/physir-splat.

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:2b3d83596d423d90436faf65da79b697CVF · CVPR 2026

Reward Forcing: Efficient Streaming Video Generation with Rewarded Distribution Matching Distillation

Lu, Yunhong, Zeng, Yanhong, Li, Haobo, Ouyang, Hao, Wang, Qiuyu, Cheng, Ka Leong, Zhu, Jiapeng, Cao, Hengyuan 等 12 位作者

原文摘要

Efficient streaming video generation is critical for simulating interactive and dynamic worlds. Existing methods distill few-step video diffusion models with sliding window attention, using initial frames as sink tokens to maintain attention performance and reduce error accumulation. However, video frames become overly dependent on these static tokens, resulting in copied initial frames and diminished motion dynamics. To address this, we introduce Reward Forcing, a novel framework with two key designs. First, we propose EMA-Sink, which maintains fixed-size tokens initialized from initial frames and continuously updated by fusing evicted tokens via exponential moving average as they exit the sliding window. Without additional computation cost, EMA-Sink tokens capture both long-term context and recent dynamics, preventing initial frame copying while maintaining long-horizon consistency. Second, to better distill motion dynamics from teacher models, we propose a novel Rewarded Distribution Matching Distillation (Re-DMD). Vanilla distribution matching treats every training sample equally, limiting the model's ability to prioritize dynamic content. Instead, Re-DMD biases the model's output distribution toward high-reward regions by prioritizing samples with greater dynamics rated by a vision-language model. Re-DMD significantly enhances motion quality while preserving data fidelity. We include both quantitative and qualitative experiments to show that Reward Forcing achieves state-of-the-art performance on standard benchmarks while enabling high-quality streaming video generation at 23.1 FPS on a single H100 GPU.

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:2f557c482e163979d76834b84a52c85eCVF · CVPR 2026

Multi-speaker Attention Alignment for Multimodal Social Interaction

Ouyang, Liangyang, Huang, Yifei, Zhang, Mingfang, Kang, Caixin, Furuta, Ryosuke, Sato, Yoichi

原文摘要

Understanding social interaction in video requires reasoning over a dynamic interplay of verbal and non-verbal cues: who is speaking, to whom, and with what gaze or gestures.While Multimodal Large Language Models (MLLMs) are natural candidates, simply adding visual inputs yields surprisingly inconsistent gains on social tasks. Our quantitative analysis of cross-modal attention inside state-of-the-art MLLMs reveals a core failure mode: in multi-speaker scenes, visual and textual tokens lack speaker-consistent alignment, exhibiting substantially weaker cross-modal attention than in object-centric images.To address this, we propose a multimodal multi-speaker attention alignment method that can be integrated into existing MLLMs. First, we introduce dynamic cross-modal head selection to identify attention heads most responsible for grounding. Then, an adaptive social-aware attention bias, computed from existing attention patterns and speaker locations, is injected into the attention mechanism. This bias reinforces alignment between a speaker's visual representation and their utterances without introducing trainable parameters or architectural changes.We integrate our method into three distinct MLLMs (LLaVA-NeXT-Video, Qwen2.5-VL, and InternVL3) and evaluate on three benchmarks (TVQA+, MMSI, OnlineMMSI). Across four social tasks, results demonstrate that our approach improves the ability of MLLMs and achieves state-of-the-art results.Attention visualizations confirm our method successfully focuses the model on speaker-relevant regions, enabling more robust multi-party social reasoning.

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:2f6f350a4b95210eb5f1af865a643499CVF · CVPR 2026

DMGD: Train-Free Dataset Distillation with Semantic-Distribution Matching in Diffusion Models

Wang, Qichao, Lu, Yunhong, Cao, Hengyuan, Zhang, Junyi, Zhang, Min

原文摘要

Dataset distillation enables efficient training by distilling the information of large-scale datasets into significantly smaller synthetic datasets. Diffusion based paradigms have emerged in recent years, offering novel perspectives for dataset distillation. However, they typically necessitate additional fine-tuning stages, and effective guidance mechanisms remain underexplored. To address these limitations, we rethink diffusion based dataset distillation and propose a Dual Matching Guided Diffusion (DMGD) framework, centered on efficient training-free guidance. We first establish Semantic Matching via conditional likelihood optimization, eliminating the need for auxiliary classifiers. Furthermore, we propose a dynamic guidance mechanism that enhances the diversity of synthetic data while maintaining semantic alignment. Simultaneously, we introduce an optimal transport (OT) based Distribution Matching approach to further align with the target distribution structure. To ensure efficiency, we develop two enhanced strategies for diffusion based framework: Distribution Approximate Matching and Greedy Progressive Matching. These strategies enable effective distribution matching guidance with minimal computational overhead. Experimental results on ImageNet-Woof, ImageNet-Nette, and ImageNet-1K demonstrate that our training-free approach achieves significant improvements, outperforming state-of-the-art (SOTA) methods requiring additional fine-tuning by average accuracy gains of 2.1%, 5.4%, and 2.4%, respectively.

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:332e5ecb0ab3f32b8f2096a95fbfd346CVF · CVPR 2026

SARL-STG: A Spatially Aware Reinforcement Learning Framework for Refining MLLMs in Spatio-Temporal Video Grounding

Gao, Hong, Xu, Xiangkai, Zhong, Bin, Yin, Junjie, Kang, Fangyu, Xu, Yutong, Dong, Xiugang, Gao, Xurui 等 9 位作者

原文摘要

Spatio-Temporal Video Grounding (STVG) requires models to localize objects both spatially and temporally. Despite recent progress, existing methods struggle with complex and fine-grained spatial semantics in language descriptions, leading to error propagation from temporal to spatial grounding stages. We attribute this limitation to the lack of iterative refinement between temporal and spatial predictions. To address these challenges, we propose SARL-STG, the first RL-based framework for STVG. It progressively refines spatio-temporal grounding through multi-stage training with dynamic interaction between temporal and spatial modules. Specifically, SARL-STG contains: (1) a unified architecture that seamlessly integrates a pretrained MLLM for temporal reasoning with an open-vocabulary detector for spatial localization, (2) a hierarchical RL training strategy that progresses from coarse temporal to fine-grained spatio-temporal optimization, and (3) a spatial knowledge-injected reward mechanism that uses spatial grounding quality as discriminative signals for temporal refinement. To facilitate training at scale, we also construct STVG-Wild, a large-scale dataset with diverse spatio-temporal annotations. Experiments demonstrate that our method achieves state-of-the-art performance on multiple benchmarks (HCSTVG, VidSTG, Charades-STA, etc.), significantly reducing error accumulation and enhancing both temporal and spatial grounding accuracy.

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:3c34333cbbffb6f41c5dc67936a7f8aeCVF · CVPR 2026

InstAP: Instance-Aware Vision-Language Pre-Train for Spatial-Temporal Understanding

Kumar, Ashutosh, Saini, Rajat, Pan, Jingjing, Erdogan, Mustafa, Zhang, Mingfang, Le Dem, Betty, Kobori, Norimasa, Kong, Quan

原文摘要

Current vision-language pre-training (VLP) paradigms excel at global scene understanding but struggle with instance-level reasoning due to global-only supervision. We introduce InstAP, an Instance-Aware Pre-training framework that jointly optimizes global vision-text alignment and fine-grained, instance-level contrastive alignment by grounding textual mentions to specific spatial-temporal regions. To support this, we present InstVL, a large-scale dataset (2 million images, 50,000 videos) with dual-granularity annotations: holistic scene captions and dense, grounded instance descriptions. On the InstVL benchmark, InstAP substantially outperforms existing VLP models on instance-level retrieval, and also surpasses a strong VLP baseline trained on the exact same data corpus, isolating the benefit of our instance-aware objective. Moreover, instance-centric pre-training improves global understanding: InstAP achieves competitive zero-shot performance on multiple video benchmarks, including MSR-VTT and DiDeMo. Qualitative visualizations further show that InstAP localizes textual mentions to the correct instances, while global-only models exhibit more diffuse, scene-level attention.

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:4e7637fff8d2ebb020adbaea2e536399CVF · CVPR 2026

NeuroRule: Bridging Vision and Logic with Differentiable Rule Induction

Zarar, Muhammad, Zhang, Mingzheng, Zhang, Xiaowang, Feng, Zhiyong

原文摘要

Scene Graph Generation (SGG) aims to structurally represent visual scenes by detecting objects and their pairwise relationships. Despite significant progress, current models encode visual knowledge with ambiguous visual context and logically inferred implicit relations due to their purely neural, pipeline-based nature. This limitation underscores the need to advance beyond identifying what relations exist to explaining why they exist and how they can be compositionally reasoned about through logical rule chaining. To address these challenges, we introduce NeuroRule, the first Neurally-Guided Rule Induction Network that integrates Mask2Former pixel-precise visual understanding with a differentiable rule induction engine. Our proposed method enables automatic learning of compositional logical rules directly from visual data while providing transparent explanations for relational predictions. NeuroRule introduces three key innovations: (1) a neural-symbolic bridge that maps visual features to probabilistic symbolic representations; (2) a differentiable rule-learning mechanism that automatically discovers interpretable first-order logic rules without manual engineering; and (3) a compositional chain rule system that enables complex inference while propagating confidence scores through an end-to-end trainable pipeline. Extensive experiments on the benchmark datasets, including Visual Genome (VG), Panoptic Scene Graph (PSG), and OpenPSG, demonstrate that NeuroRule achieves state-of-the-art performance. Our method significantly improves few-shot relation extraction while maintaining full interpretability in its rule-based explanations.

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:5163fe612070d076c2ec984b82704d78CVF · CVPR 2026

DC-Merge: Improving Model Merging with Directional Consistency

Zhang, Han-Chen, Zhou, Zi-Hao, Luo, Mao-Lin, Di, Shimin, Zhang, Min-Ling, Wei, Tong

原文摘要

Model merging aims to integrate multiple task-adapted models into a unified model that preserves the knowledge of each task. In this paper, we identify that the key to this knowledge retention lies in maintaining the directional consistency of singular spaces between merged multi-task vector and individual task vectors. However, this consistency is frequently compromised by two issues: i) an imbalanced energy distribution within task vectors, where a small fraction of singular values dominate the total energy, leading to the neglect of semantically important but weaker components upon merging, and ii) the geometric inconsistency of task vectors in parameter space, which causes direct merging to distort their underlying directional geometry. To address these challenges, we propose DC-Merge, a method for directional-consistent model merging. It first balances the energy distribution of each task vector by smoothing its singular values, ensuring all knowledge components are adequately represented. These energy-balanced vectors are then projected onto a shared orthogonal subspace to align their directional geometries with minimal reconstruction error. Finally, the aligned vectors are aggregated in the shared orthogonal subspace and projected back to the original parameter space. Extensive experiments on vision and vision-language benchmarks show that DC-Merge consistently achieves state-of-the-art performance in both full fine-tuning and LoRA settings. The implementation code is available at https://github.com/Tobeginwith/DC-Merge.

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:54f698a94cd5f68092bbc640b026fcc5CVF · CVPR 2026

TINA: Text-Free Inversion Attack for Unlearned Text-to-Image Diffusion Models

Xiang, Qianlong, Zhang, Miao, Zhang, Haoyu, Wang, Kun, Hou, Junhui, Nie, Liqiang

原文摘要

Although text-to-image diffusion models exhibit remarkable generative power, concept erasure techniques are essential for their safe deployment to prevent the creation of harmful content.This has fostered a dynamic interplay between the development of erasure defenses and the adversarial probes designed to bypass them, and this co-evolution has progressively enhanced the efficacy of erasure methods.However, this adversarial co-evolution has converged on a narrow, text-centric paradigm that equates erasure with severing the text-to-image mapping, ignoring that the underlying visual knowledge related to undesired concepts still persist.To substantiate this claim, we investigate from a visual perspective, leveraging DDIM inversion to probe whether a generative pathway for the erased concept can still be found.However, identifying such a visual generative pathway is challenging because standard text-guided DDIM inversion is actively resisted by text-centric defenses within the erased model.To address this, we introduce TINA, a novel Text-free INversion Attack, which enforces this visual-only probe by operating under a null-text condition, thereby avoiding existing text-centric defenses.Moreover, TINA integrates an optimization procedure to overcome the accumulating approximation errors that arise when standard inversion operates without its usual textual guidance.Our experiments demonstrate that TINA successfully regenerates erased concepts from models treated with state-of-the-art unlearning.The success of TINA proves that current methods merely obscure concepts, highlighting an urgent need for paradigms that operate directly on internal visual knowledge.Code will be released upon acceptance.

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:5ceb38e224e635c85a453c6b4a5ed43dCVF · CVPR 2026

EpiAgent: An Agent-Centric System for Ancient Inscription Restoration

Zhu, Shipeng, Chen, Ang, Nie, Na, Fang, Pengfei, Zhang, Min-Ling, Xue, Hui

原文摘要

Ancient inscriptions, as repositories of cultural memory, have suffered from centuries of environmental and human-induced degradation. Restoring their intertwined visual and textual integrity poses one of the most demanding challenges in digital heritage preservation. However, existing AI-based approaches often rely on rigid pipelines, struggling to generalize across such complex and heterogeneous real-world degradations. Inspired by the skill-coordinated workflow of human epigraphers, we propose EpiAgent, an agent-centric system that formulates inscription restoration as a hierarchical planning problem. Following an Observe-Conceive-Execute-Reevaluate paradigm, an LLM-based central planner orchestrates collaboration among multimodal analysis, historical experience, specialized restoration tools, and iterative self-refinement. This agent-centric coordination enables a flexible and adaptive restoration process beyond conventional single-pass methods. Across real-world degraded inscriptions, EpiAgent achieves superior restoration quality and stronger generalization compared to existing methods. Our work marks an important step toward expert-level agent-driven restoration of cultural heritage. The code is available at https://github.com/blackprotoss/EpiAgent.

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:61e06ddead47ea3f96215cf21c9989a9CVF · CVPR 2026

CAST: Context-Aware Dynamic Latent Space Transformation for Interactive Text-to-Image Retrieval

Lin, Xuanzuo, Zhang, Min, Liu, Daizong, Zuo, Zhiwen, Yang, Xun, Lin, Changting, Wang, Xun, Dong, Jianfeng

原文摘要

Interactive Text-to-Image Retrieval (I-TIR) aims to refine image retrieval results through natural language dialogues, which allows users to progressively supplement or correct their search intention across multiple rounds, enabling a more precise and user-aligned visual search experience.However, existing methods perform cross-modal retrieval within a fixed multimodal feature space, mapping all dialogue text and images onto the same static embedding manifold.Such static formulation easily causes semantic vagueness, making it difficult to capture subtle embedding shifts in the user's updated intention for fine-grained retrieval.To address this limitation, we propose Context-Aware Latent Space Transformation (CAST), a lightweight framework that dynamically transforms the common latent space of both textual and visual representations according to the specific evolving user's search intention, enabling fine-grained and adaptive semantic alignment.The core of CAST is the Context-Aware Space Regulator (CASR), a crucial space transformation module composed of two key components: (1) the Context-Aware Low-Rank Projector (CLP), which learns to predict the projection direction of embedding space based on the intent's context;and (2) the Context-Guided Modulator (CGM), which adaptively determines appropriate projection strength. CASR is highly lightweight, adding negligible parameters and computational overhead, and can be seamlessly integrated into diverse I-TIR frameworks.Extensive experiments demonstrate the effectiveness of our proposed framework, indicating that it can serve as a general, plug-and-play solution for efficient and scalable interactive text-to-image retrieval. Our source code is available at https://github.com/HuiGuanLab/CAST.

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:625ee3c6c1e06850c51c54cd8354a5d6CVF · CVPR 2026

CaST-Bench: Benchmarking Causal Chain-Grounded Spatio-Temporal Reasoning for Video Question Answering

Zhang, Mingfang, Pan, Jingjing, Kumar, Ashutosh, Saini, Rajat, Erdogan, Mustafa, Yang, Hsuan-Kung, Kang, Caixin, Huang, Yifei 等 10 位作者

原文摘要

Cause-and-effect reasoning in video is a significant challenge for Vision-Language Models (VLMs), as it requires going beyond surface-level perception to a deeper understanding of causal mechanisms. However, existing benchmarks rarely provide the fine-grained, grounded evidence needed to rigorously evaluate this capability. To address this gap, we introduce CaST-Bench, a benchmark for Causal Chain-Grounded Spatio-Temporal Video Reasoning. CaST-Bench presents complex causal questions that require models to identify and localize a chain of multiple spatio-temporal evidences. Through a human-AI collaborative pipeline, we construct a high-quality dataset of 2,066 questions over 1,015 videos, with causal chains annotated by temporal segments and bounding-box tracks. Furthermore, we design a comprehensive evaluation suite with novel metrics that assess not only answer correctness but also the capability for visual evidence grounded reasoning. This grounding is crucial for improving accuracy by mitigating spurious correlations and for enhancing user trust by making models more transparent. Our experiments show that current VLMs struggle with causal questions, largely due to their limited ability to construct precise and grounded causal chains. This highlights an important direction for improving future VLMs.

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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SafeRoPE: Risk-specific Head-wise Embedding Rotation for Safe Generation in Rectified Flow Transformers

Yang, Xiang, Li, Feifei, Zhang, Mi, Hong, Geng, You, Xiaoyu, Yang, Min

原文摘要

Recent Text-to-Image (T2I) models based on rectified-flow transformers (e.g., SD3, FLUX) achieve high generative fidelity but remain vulnerable to unsafe semantics, especially when triggered by multi-token interactions. Existing mitigation methods largely rely on fine-tuning or attention modulation for concept unlearning; however, their expensive computational overhead and design tailored to U-Net-based denoisers hinder direct adaptation to transformer-based diffusion models (e.g., MMDiT). In this paper, we conduct an in-depth analysis of the attention mechanism in MMDiT and find that unsafe semantics concentrate within interpretable, low-dimensional subspaces at head level, where a finite set of safety-critical heads is responsible for unsafe feature extraction. We further observe that perturbing the Rotary Positional Embedding (RoPE) applied to the query and key vectors can effectively modify some specific concepts in the generated images. Motivated by these insights, we propose SafeRoPE, a lightweight and fine-grained safe generation framework for MMDiT. Specifically, SafeRoPE first constructs head-wise unsafe subspaces by decomposing unsafe embeddings within safety-critical heads, and computes a Latent Risk Score (LRS) for each input vector via projection onto these subspaces. We then introduce head-wise RoPE perturbations that can suppress unsafe semantics without degrading benign content or image quality. SafeRoPE combines both head-wise LRS and RoPE perturbations to perform risk-specific head-wise rotation on query and key vector embeddings, enabling precise suppression of unsafe outputs while maintaining generation fidelity. Extensive experiments demonstrate that SafeRoPE achieves SOTA performance in balancing effective harmful content mitigation and utility preservation for safe generation of MMDiT. Codes are available at https://github.com/deng12yx/SafeRoPE.

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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IPR-1: Interactive Physical Reasoner

Zhang, Mingyu, Zhuo, Lifeng, Tan, Tianxi, Xie, Guocan, Nie, Xian, Li, Yan, Zhao, Renjie, He, Zizhu 等 11 位作者

原文摘要

Humans learn by observing, interacting with environments, and internalizing physics and causality. We explore whether agents can similarly acquire human-like reasoning through interaction and experience. To study this, we introduce a Game-to-Unseen (G2U) benchmark of 1,000+ heterogeneous games that exhibit significant visual domain gaps. Existing approaches, including VLMs and world models, struggle with underlying physics and causality due to overfitting on visual details. VLM/VLA agents reason but lack look-ahead in interactive settings, while world models imagine but imitate visual patterns rather than analyze physics and causality. We therefore propose IPR (Interactive Physical Reasoner), using world-model rollouts to score and reinforce a VLM's policy, and introduce PhysCode, a physics-centric action code aligning semantic intent with dynamics to provide a shared action space for prediction and reasoning. Pretrained on 1,000+ games, IPR performs robustly on levels from primitive intuition to goal-driven reasoning, and even surpasses GPT-5 overall. We find that performance improves with more training games and interaction steps, and that the model also zero-shot transfers to unseen games. These results support physics-centric interaction as a path to steadily improving physical reasoning. Further demos and details can be found at https://mybearyzhang.github.io/ipr-1.

cs.CV

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

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MoCapAnything: Unified 3D Motion Capture for Arbitrary Skeletons from Monocular Videos

Gong, Kehong, Wen, Zhengyu, He, Weixia, Xu, Mingxi, Wang, Qi, Zhang, Ning, Li, Zhengyu, Lian, Dongze 等 11 位作者

原文摘要

Motion capture now underpins content creation far beyond digital humans, yet most pipelines remain species- or template-specific. We formalize this gap as Category-Agnostic Motion Capture (CAMoCap): given a monocular video and an arbitrary rigged 3D asset as a prompt, the goal is to reconstruct a rotation-based animation (e.g., BVH) that directly drives the specific asset. We present MoCapAnything, a reference-guided, factorized framework that first predicts 3D joint trajectories and then recovers asset-specific rotations via constraint-aware Inverse Kinematics (IK) Fitting. MoCapAnything comprises three learnable modules and a lightweight IK stage: a Reference Prompt Encoder that distills per-joint queries from the asset's skeleton, mesh, and rendered image set; a Video Feature Extractor that computes dense visual descriptors and reconstructs a coarse 4D deforming mesh to bridge the modality gap between RGB tokens and the point-cloud-like joint space; and a Unified Motion Decoder that fuses these cues to produce temporally coherent trajectories. We also curate Truebones Zoo with 1,038 motion clips, each providing a standardized skeleton-mesh-rendered-video triad. Experiments on in-domain benchmarks and in-the-wild videos show that MoCapAnything delivers high-quality skeletal animations and exhibits non-trivial cross-species retargeting across heterogeneous rigs, offering a scalable path toward prompt-based 3D motion capture for arbitrary assets. The codes are available on our project page: https://animotionlab.github.io/MoCapAnything/

cs.CV

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

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Parameter-Efficient Adaptation for MLLMs via Implicit Modality Decomposition

Zhang, Mingfang, Wang, Yunhong, Wang, Lu, Chen, Jiaxin

原文摘要

Parameter-efficient fine-tuning (PEFT) has become a compelling approach for adapting large language models (LLMs) into multimodal large language models (MLLMs), enabling them to handle diverse modalities with substantially lower memory and computational costs. However, most existing PEFT methods neglect the issue of modality-imbalanced learning, which is characterized by the excessive dominance of text modality in updating parameters, thus incurring insufficient learning of non-text modalities and leading to performance degradation. To address this issue, we propose a novel parameter-efficient adaptation method for MLLMs, namely Implicit Modality Decomposition (IMoD), based on LoRA. It firstly decomposes the learnable parameters into the non-overlapped text-specific, non-text-specific and modality-sharing components, thereby alleviating modality imbalance. To further guide the optimization of these components toward specific modalities, we propose Modality-Specific Decoupling Constraint that suppresses cross-modal interference among modality-specific parameters, and Modality-Agnostic Alignment Constraint that encourages modality-sharing component to capture well-aligned, modality-invariant semantics. Extensive experiments across diverse multimodal settings and LLM architectures demonstrate that our method consistently delivers significant performance gains, particularly achieving an averaged 3.3% improvements on the audio-visual-text tasks without sacrificing the parameter and inference efficiency. Code is available at https://github.com/mmffzzz/IMoD.git.

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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Distributed Image Compression with Multimodal Side Information at Extremely Low Bitrates

Xu, Guojun, Zhang, Mingyang, Xiang, Jianwen, Tan, Cheng, Yang, Yanchao, Zhou, Junwei

原文摘要

Distributed Image Compression (DIC) is crucial for multi-view transmission, especially when operating at extremely low bitrates (< 0.1 bpp). Its core challenge is effectively utilizing side information to achieve high-quality reconstruction under strict bitrate budgets. However, existing DIC approaches struggle to exploit global context and object-level details from side information, leading to local blurring and the loss of fine details in the reconstruction. To address these limitations, we propose a Multimodal DIC framework (MDIC), which, for the first time, leverages side information in a multimodal manner into the DIC paradigm, effectively preserving fine-grained local details and enhancing global perceptual quality in reconstructed images. Specifically, we introduce a text-to-image diffusion-based decoder conditioned on textual side information extracted from correlated images to capture shared global semantics. Moreover, we design a feature-mask generator, supervised by a multimodal fine-grained alignment task, to strengthen the exploitation of visual side information. The generated mask serves two purposes: first, it guides the extraction of fine-grained details from losslessly transmitted side information to preserve the semantic consistency of reconstructed details; second, it regulates the extraction of clustered feature representations from the quantized VQ-VAE embeddings, compensating for category information lost under the extreme compression of the primary image. Extensive experiments on the widely used KITTI Stereo and Cityscapes datasets demonstrate that MDIC achieves state-of-the-art perceptual quality at extremely low bitrates.

cs.CV

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

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AdapAction: Adaptive Target Action Backdoor Attack against GUI Agents

Chen, Baicheng, Zhang, Mingda, Zhang, Min, Li, Haizhou, Wu, Baoyuan

原文摘要

Autonomous Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) are increasingly vital for complex task automation. However, their capacity for self-driven decision-making introduces significant, yet underexplored, security risks, among which backdoor attacks pose a particularly stealthy and high-impact threat. Prior work has shown GUI agents vulnerable to such attacks, but existing methods rely on static trigger-action mappings that execute fixed, context-agnostic behaviors, making them highly detectable. To address this limitation, we introduce **AdapAction**, a novel backdoor attack that subverts the agent's decision-making by embedding an **adaptive, context-aware policy**. Unlike traditional approaches, AdapAction enables the agent to autonomously select environmentally coherent malicious actions based on the current GUI state and user instruction, thereby evading detection while preserving functional utility. Extensive experiments on the Android-In-The-Zoo (AitZ) and AndroidControl benchmarks show that AdapAction achieves up to 100% Attack Success Rate (ASR) while preserving benign task utility. More critically, AdapAction consistently evades a multi-principle-based LLM defense evaluating instruction alignment, visual coherence, and safety, whereas traditional fixed-action attacks are easily detected. This resilience stems from AdapAction's contextually grounded malicious actions, which are semantically and visually indistinguishable from legitimate operations. As a result, AdapAction exhibits exceptional stealth and poses a significantly greater real-world threat to LLM-powered GUI agents.

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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OmniGround: A Comprehensive Spatio-Temporal Grounding Benchmark for Real-World Complex Scenarios

Gao, Hong, Wu, Jingyu, Xu, Xiangkai, Xie, Kangni, Zhang, Yunchen, Zhong, Bin, Gao, Xurui, Zhang, Min-Ling

原文摘要

Spatio-Temporal Video Grounding (STVG) aims to localize target objects in videos based on natural language descriptions. While Multimodal Large Language Models have shown promise, a significant gap remains between current models and real-world demands involving diverse objects and complex queries. We attribute this to limited benchmark scope, causing models to exhibit category bias, oversimplified reasoning, and poor linguistic robustness.To address these limitations, we introduce OmniGround, a comprehensive benchmark with 3,475 videos spanning 81 categories and complex real-world queries. We propose the Forward-Backward-Refinement (FBR) annotation pipeline for high-quality labels and DeepSTG, a systematic evaluation framework quantifying dataset quality beyond superficial statistics. Evaluations reveal performance average drops of 10.4% on complex real-world scenes, particularly with small/occluded objects and intricate spatial relations. Motivated by these, we propose PG-TAF, a training-free two-stage framework decomposing STVG into high-level temporal grounding and fine-grained spatio-temporal propagation. Experiments demonstrate PG-TAF achieves 25.6% and 35.6% improvements in m_tIoU and m_vIoU on OmniGround with consistent gains across four 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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