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首次观测 · 记录 #1CVF · CVPR 2026:cvf:504d31e5684b54393db6015bd5460d4cCVF · CVPR 2026

AREA3D: Active Reconstruction Agent with Unified Feed-Forward 3D Perception and Vision-Language Guidance

Xu, Tianling, Gan, Shengzhe, Gu, Leslie, Li, Yuelei, Zhan, Fangneng, Pfister, Hanspeter

原文摘要

Active 3D reconstruction enables an agent to autonomously select viewpoints to build accurate and complete scene geometry efficiently, rather than passively reconstructing scenes from pre-collected images. Existing active reconstruction methods often rely on geometric heuristics, which may result in redundant observations without improving reconstruction quality. To address this, we propose AREA3D, an active reconstruction agent for 3D reconstruction by leveraging feed-forward 3D models and vision-language guidance. The framework decouples view uncertainty modeling from feed-forward reconstruction, enabling precise uncertainty estimation without online optimization. Moreover, the integrated Vision-Language Model provides high-level semantic guidance that guides exploration beyond purely geometric cues. Extensive experiments on both scene-level and object-level benchmarks (Replica and OmniObject3D) demonstrate that AREA3D achieves state-of-the-art reconstruction accuracy, especially in sparse views.

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:50819da629c863b9b01a91b034a72421CVF · CVPR 2026

Beyond Single Images: A Comprehensive Benchmark for Album-Level Vision-Language Understanding

Huang, Shawn, Price, Brian, Fan, Yifei, Morse, Bryan

原文摘要

Automatic album organization has been studied extensively over the past decades due to significant progress in digital photography. Recent vision-language models (VLMs) have shown strong performance on multi-image understanding, making them natural candidates for automating album organization workflows. While VLMs' abilities in multi-image understanding have been widely studied, their performance on album organization remains underexplored. To bridge this gap, we introduce AlbumBench, the first comprehensive benchmark for automatic album organization. Specifically, we (1) define album organization tasks as photo selection for album-specific user objectives, photo rating according to how well user intents are fulfilled, and album-specific photo grouping given a user query which requires contextual understanding of the album; (2) establish AlbumBench, a benchmark dataset containing 27051 images across 641 albums with 5 annotations per image; and (3) evaluate mainstream open-source and proprietary VLMs on AlbumBench. We show that AlbumBench presents unique challenges compared to traditional multi-image understanding benchmarks due to its requirement for understanding album context and user intent. Our findings reveal a significant performance gap between open-source and proprietary VLMs on album organization tasks. Despite this gap, even the best-performing proprietary models sometimes struggle with tasks that humans find relatively easy. We hope that AlbumBench can serve as a foundation for unifying album organization research and motivate improvements in VLMs' performance on these tasks.

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:50c17ae6a147f3a1f849fda726ac83f5CVF · CVPR 2026

Language-Guided One-Step Diffusion Model for Nighttime Flare Removal

Ning, Aoxiang, Yu, Kailong, Xue, Minglong, Pan, Liyuan, He, Jinhong, Yan, Wenchao, Zhou, Mingliang, Wu, Yirui

原文摘要

Nighttime photography is susceptible to flare caused by strong light sources, which degrades visual quality and disrupts structural information required by downstream vision tasks. Existing nighttime flare removal methods generally lack semantic priors for flare-occluded regions and thus tend to introduce artifacts and lose details under severe degradation. To address this problem, we propose a language-guided one-step diffusion framework that explicitly aligns flare-occluded regions with the underlying scene content at the semantic level. Specifically, we develop the first flare-specific vision-language model, Flare-VLM, which extracts fine-grained textual descriptions to guide one-step diffusion for high-quality restoration of severely damaged areas. Then, we propose semantics-aware distribution distillation to constrain the noise distribution with high-level semantics, suppressing redundant perturbations on clean backgrounds and improving the stability of distillation. In addition, we design an instruction-driven data synthesis pipeline to generate geometrically and semantically aligned nighttime flare samples, narrowing the gap to real degradations. Experimental results demonstrate that the proposed method achieves better restoration and enhances the performance of downstream vision tasks.

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:522e19544404df2795a8e236f9b4c0a0CVF · CVPR 2026

HOG-Layout: Hierarchical 3D Scene Generation, Optimization and Editing via Vision-Language Models

Jiang, Haiyan, Zhang, Deyu, Weng, Dongdong, Song, Weitao, Duh, Henry Been-Lirn

原文摘要

3D layout generation and editing play a crucial role in Embodied AI and immersive VR interaction. However, manual creation requires tedious labor, while data-driven generation often lacks diversity. The emergence of large models introduces new possibilities for 3D scene synthesis. We present HOG-Layout that enables text-driven hierarchical scene generation, optimization and real-time scene editing with large language models (LLMs) and vision-language models (VLMs). HOG-Layout improves scene semantic consistency and plausibility through retrieval-augmented generation (RAG) technology, incorporates an optimization module to enhance physical consistency, and adopts a hierarchical representation to enhance inference and optimization, achieving real-time editing. Experimental results demonstrate that HOG-Layout produces more reasonable environments compared with existing baselines, while supporting fast and intuitive scene editing.

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:526e990191f3e297f54dae1919d7c2f5CVF · CVPR 2026

Progress by Pieces: Test-Time Scaling for Autoregressive Image Generation

Park, Joonhyung, Jang, Hyeongwon, Kim, Joowon, Yang, Eunho

原文摘要

Recent visual autoregressive (AR) models have shown promising capabilities in text-to-image generation, operating in a manner similar to large language models. While test-time computation scaling yields higher-quality outputs on challenging natural language tasks, its adaptation to visual AR models remains unexplored and poses unique challenges. Naively applying test-time scaling strategies such as Best-of-N can be suboptimal: they consume full-length computation on erroneous generation trajectories, while the raster-scan decoding scheme lacks a blueprint of the entire canvas, limiting scaling benefits as only a few prompt-aligned candidates are generated. To address these, we introduce GridAR, a test-time scaling framework designed to elicit the best possible results from visual AR models. GridAR employs a grid-partitioned progressive generation scheme in which multiple partial candidates for the same position are generated within a canvas, infeasible ones are pruned early, and viable ones are fixed as anchors to guide subsequent decoding. Coupled with this, we present a layout-specified prompt reformulation strategy that inspects partial views to infer a feasible layout for satisfying the prompt. The reformulated prompt guides subsequent image generation to mitigate the blueprint deficiency. GridAR achieves higher-quality results under limited test-time scaling: with N=4, it even outperforms Best-of-N (N=8) by 14.4% on T2I-CompBench++ while reducing cost by 25.6%. It also generalizes to autoregressive image editing, showing a 14.5% gain in semantic preservation on PIE-Bench over larger-N baselines.

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:52e52b93df94cdad3283b08d4e3c7a3fCVF · CVPR 2026

No Need For Real Anomaly: MLLM Empowered Zero-Shot Video Anomaly Detection

Dai, Zunkai, Li, Ke, Liu, Jiajia, Yang, Jie, Qiao, Yuanyuan

原文摘要

The collection and detection of video anomaly data has long been a challenging problem due to its rare occurrence and spatio-temporal scarcity. Existing video anomaly detection (VAD) methods under perform in open-world scenarios. Key contributing factors include limited dataset diversity, and inadequate understanding of context-dependent anomalous semantics. To address these issues, i) we propose LAVIDA, an end-to-end zero-shot video anomaly detection framework. ii) LAVIDA employs an Anomaly Exposure Sampler that transforms segmented objects into pseudo-anomalies to enhance model adaptability to unseen anomaly categories. It further integrates a Multimodal Large Language Model (MLLM) to bolster semantic comprehension capabilities. Additionally, iii) we design a token compression approach based on reverse attention to handle the spatio-temporal scarcity of anomalous patterns and decrease computational cost. The training process is conducted solely on pseudo anomalies without any VAD data. Evaluations across four benchmark VAD datasets demonstrate that LAVIDA achieves SOTA performance in both frame-level and pixel-level anomaly detection under the zero-shot setting. Our code is available in https://github.com/VitaminCreed/LAVIDA.

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:530b7a6fed8696451d354fc99dfac8c4CVF · CVPR 2026

Unified Vector Floorplan Generation via Markup Representation

Shiohara, Kaede, Yamasaki, Toshihiko

原文摘要

Automatic residential floorplan generation has long been a central challenge bridging architecture and computer graphics, aiming to make spatial design more efficient and accessible. While early methods based on constraint satisfaction or combinatorial optimization ensure feasibility, they lack diversity and flexibility. Recent generative models achieve promising results but struggle to generalize across heterogeneous conditional tasks, such as generation from site boundaries, room adjacency graphs, or partial layouts, due to their suboptimal representations. To address this gap, we introduce Floorplan Markup Language (FML), a general representation that encodes floorplan information within a single structured grammar, which casts the entire floorplan generation problem into a next token prediction task. Leveraging FML, we develop a transformer-based generative model, Floorplan Markup Language Model (FMLM), capable of producing high-fidelity and functional floorplans under diverse conditions. Comprehensive experiments on the RPLAN dataset demonstrate that FMLM, despite being a single model, surpasses the previous task-specific state-of-the-art 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:536ef64a56eaf4922ea6853da352d2e2CVF · CVPR 2026

LAMP: Language-Assisted Motion Planning for Controllable Video Generation

Kizil, Muhammed Burak, Sanli, Enes, Mitra, Niloy J., Erdem, Erkut, Erdem, Aykut, Ceylan, Duygu

原文摘要

Video generation has achieved remarkable progress in visual fidelity and controllability, enabling conditioning on text, layout, or motion. Among these, motion control -- specifying object dynamics and camera trajectories -- is essential for composing complex, cinematic scenes, yet existing interfaces remain limited. We introduce LAMP, that leverages large language models (LLMs) as motion planners to translate natural language descriptions into explicit 3D trajectories for dynamic objects and (relatively defined) cameras. LAMP defines a motion domain-specific language (DSL), inspired by cinematography conventions. By harnessing program synthesis capabilities of LLMs, LAMP generates structured motion programs from natural language, which are deterministically mapped to 3D trajectories. We construct a large-scale procedural dataset pairing natural text descriptions with corresponding motion programs and 3D trajectories. Experiments demonstrate LAMP's improved performance in motion controllability and alignment with user intent compared to state-of-the-art alternatives, establishing the first framework for generating both object and camera motions directly from natural language specifications. Video and code are available at the project page at https://cyberiada.github.io/LAMP/ .

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:53d3a274e781a6bdafe8506e98dbb0f4CVF · CVPR 2026

META: Meta Evolution of Tool Trajectory Adaptation for Long-Video Understanding

Huang, Jing, Chen, Luyuan, Xu, Zhijie, Li, Yadong, Xu, Xingzhong, Chen, Siye, Liu, Jie, Kong, Ming 等 9 位作者

原文摘要

Long-video understanding remains challenging due to extreme temporal redundancy, sparse yet decisive events, and the instability of long-horizon reasoning in visual-language models (VLMs). Existing agent-based methods invoke external micro-tools but remain static, repeatedly rebuilding long chains of fine-grained operations for each task without acquiring reusable multi-step perceptual skills.We propose META, the first training-free agent capable of self-evolving its tool-augmented reasoning. META operates through dual Solving and Evolving loops: it analyzes its own tool trajectories, abstracts recurring multi-step patterns into reusable macro-tools, and distills failed executions into structured failure priors that refine tool usage. Through symbolic consolidation and pruning, META progressively shortens reasoning paths and acquires more general perceptual and temporal abilities--without any parameter updates. META achieves state-of-the-art performance on long-video benchmarks, demonstrating a scalable, model-agnostic paradigm for long-video understanding that can continually evolve without additional training.

cs.CV · cs.CL

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:53e16fa9534b6865e27678bd5f5b234aCVF · CVPR 2026

Cluster-Wise Spatio-Temporal Masking for Efficient Video-Language Pretraining

Zhuang, Weijun, Huang, Yuqing, Meng, Weikang, Li, Xin, Liu, Ming, Hong, Xiaopeng, Wang, Yaowei, Zuo, Wangmeng

原文摘要

Large-scale video-language pretraining enables strong generalization across multimodal tasks but often incurs prohibitive computational costs. Although recent advances in masked visual modeling help mitigate this issue, they still suffer from two fundamental limitations: severe visual information loss under high masking ratios and temporal information leakage caused by inter-frame correlations. To address these challenges, we propose ClusterSTM, a Cluster-Wise Spatio-Temporal Masking strategy for efficient video-language pretraining. ClusterSTM first performs intra-frame clustering to partition visual tokens into multiple semantically independent clusters, then conducts cluster-wise masking by retaining the token with the highest temporal density within each cluster. Our masking strategy ensure that the retained tokens capture holistic video content while exhibit strong temporal correlation. Additionally, we introduce a video-text relevance reconstruction objective that aligns high-level multimodal semantics beyond conventional visual reconstruction. Extensive experiments across multiple benchmarks demonstrate that ClusterSTM achieves superior performance on video-text retrieval, video question answering, and video captioning tasks, establishing a new state-of-the-art among efficient video-language models.

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:542360c7ef167e8deb590eff3946898eCVF · CVPR 2026

A Closed-Form Solution for Debiasing Vision-Language Models with Utility Guarantees Across Modalities and Tasks

Lian, Tangzheng, Hu, Guanyu, Ren, Yijing, Kollias, Dimitrios, Celiktutan, Oya

原文摘要

While Vision-Language Models (VLMs) have achieved remarkable performance across diverse downstream tasks, recent studies have shown that they can inherit social biases from the training data and further propagate them into downstream applications. To address this issue, various debiasing approaches have been proposed, yet most of them aim to improve fairness without having a theoretical guarantee that the utility of the model is preserved. In this paper, we introduce a debiasing method that yields a closed-form solution in the cross-modal space, achieving Pareto-optimal fairness with bounded utility losses. Our method is training-free, requires no annotated data, and can jointly debias both visual and textual modalities across downstream tasks. Extensive experiments show that our method outperforms existing methods in debiasing VLMs across diverse fairness metrics and datasets for both group and intersectional fairness in downstream tasks such as zero-shot image classification, text-to-image retrieval, and text-to-image generation while preserving task performance. Code will be made available upon acceptance.

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:54412e5f4787d2ccd4da7385fe672677CVF · CVPR 2026

DiG: Differential Grounding for Enhancing Fine-Grained Perception in Multimodal Large Language Models

Tao, Zhou, Wang, Shida, Hua, YongXiang, Cao, Haoyu, Xu, Linli

原文摘要

Multimodal Large Language Models have achieved impressive performance on a variety of vision-language tasks, yet their fine-grained visual perception and precise spatial reasoning remain limited. In this work, we introduce DiG (Differential Grounding), a novel proxy task framework where MLLMs learn fine-grained perception by identifying and localizing all differences between similar image pairs without prior knowledge of their number. To support scalable training, we develop an automated 3D rendering-based data generation pipeline that produces high-quality paired images with fully controllable discrepancies. To address the sparsity of difference signals, we further employ curriculum learning that progressively increases complexity from single to multiple differences, enabling stable optimization. Extensive experiments demonstrate that DiG significantly improves model performance across a variety of visual perception benchmarks and that the learned fine-grained perception skills transfer effectively to standard downstream tasks, including RefCOCO, RefCOCO+, RefCOCOg, and general multimodal perception benchmarks. Our results highlight differential grounding as a scalable and robust approach for advancing fine-grained visual reasoning in MLLMs.

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:545a3497620c0d189dedb46bf13e5d8cCVF · CVPR 2026

PAS: Prelim Attention Score for Detecting Object Hallucinations in Large Vision-Language Models

Hoang, Nhat, Vu, Minh, Thai, My T., Bhattarai, Manish

原文摘要

Large vision-language models (LVLMs) are powerful, yet they remain unreliable due to object hallucinations. In this work, we show that in many hallucinatory predictions the LVLM effectively ignores the image and instead relies on previously generated output ("prelim") tokens to infer new objects. We quantify this behavior via the mutual information between the image and the predicted object conditioned on the prelim, demonstrating that weak image dependence strongly correlates with hallucination. Building on this finding, we introduce the Prelim Attention Score (PAS), a lightweight, training-free signal computed from attention weights over prelim tokens. PAS requires no additional forward passes and can be computed on the fly during inference. Exploiting this previously overlooked signal, PAS achieves state-of-the-art object-hallucination detection across multiple models and datasets, enabling real-time filtering and intervention.

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:546cfdafeee960ad7fba9f82e828d536CVF · CVPR 2026

DEVA: Fine-tuning Multimodal Large Language Models for Visual Perception Tasks

Das, Debasmit, Hayat, Munawar, Porikli, Fatih

原文摘要

Fine-tuning large language models (LLMs) using reinforcement learning (RL) objectives has gained traction, especially in scenarios where labeled data is limited. Building on its success in the language domain, recent efforts have extended RL-based fine-tuning to multimodal tasks. Visual-RFT, for instance, applied Group Relative Policy Optimization (GRPO) to fine-tune multimodal LLMs (MLLMs) across various visual perception benchmarks, achieving notable improvements over standard supervised fine-tuning (SFT). However, its scope was limited by a narrow evaluation of RL adaptation strategies. In this work, we expand the landscape by introducing new RL-based baselines on the same benchmarks and conducting a deeper analysis of GRPO's training dynamics. We identify key limitations--such as reduced generation diversity, constrained policy exploration, and suboptimal reward formulation and aggregation. To address these, we propose DEVA: a framework that enhances Diversity via a flow-based training objective, encourages broader policy Exploration through global entropic regularization, and leverages alignment Volume as a non-verifiable reward combined with harmonic Aggregation. Applied to GRPO and other RL methods, DEVA delivers consistent gains in both quantitative (+5 to +13 points) and qualitative metrics. We further provide visualizations, ablations, and analyses to unpack the contributions of each component in our framework.

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:54ac4c389f82ba8fa7e7aecaddf2fa7dCVF · CVPR 2026

SEASON: Mitigating Temporal Hallucination in Video Large Language Models via Self-Diagnostic Contrastive Decoding

Wu, Chang-Hsun, Chang, Kai-Po, Sheng, Yu-Yang, Chung, Hung-Kai, Wang, Kuei-Chun, Wang, Yu-Chiang Frank

原文摘要

Video Large Language Models (VideoLLMs) have shown remarkable progress in video understanding. However, these models still struggle to effectively perceive and exploit rich temporal information in videos when responding to user queries. Therefore, they often generate descriptions of events that are temporal inconsistent or causally implausible, causing severe hallucination issues. While most prior studies have focused on spatial hallucinations (e.g. object mismatches), temporal reasoning in video understanding remains relatively underexplored. To address this issue, we propose Self-Diagnostic Contrastive Decoding (SEASON), a training-free method that adaptively enhances temporal and spatial faithfulness for each output token. It achieves this by dynamically diagnosing each token's hallucination tendency and applying adaptive contrastive decoding against its corresponding temporal and spatial negatives. Extensive experiments demonstrate that SEASON outperforms all existing training-free hallucination mitigation approaches on three hallucination examination benchmarks, while further improves VideoLLMs across four general video understanding benchmarks. Our project page: https://chriswu018.github.io/season/

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:55079dc8b3e0ac5d605cde50eec3572eCVF · CVPR 2026

Probabilistic Concept Graph Reasoning for Multimodal Misinformation Detection

Yang, Ruichao, Gao, Wei, Zhu, Xiaobin, Ma, Jing, Lin, Hongzhan, Luo, Ziyang, Zhang, Bo-Wen, Yin, Xu-Cheng

原文摘要

Multimodal misinformation poses an escalating challenge that often evades traditional detectors, which are opaque black boxes and fragile against new manipulation tactics. We present Probabilistic Concept Graph Reasoning (PCGR), an interpretable, modular, and evolvable framework that reframes multimodal misinformation detection (MMD) as structured, concept-based reasoning. PCGR follows a build-then-infer paradigm, which first constructs a graph of human-understandable concept nodes, including novel high-level concepts automatically discovered and validated by multimodal large language models (MLLMs), and then applies hierarchical attention over this concept graph to infer claim veracity. This design produces interpretable reasoning chains linking evidence to conclusions. Experiments demonstrate that PCGR achieves state-of-the-art MMD accuracy and robustness to emerging manipulation types, outperforming prior methods in both coarse detection and fine-grained manipulation recognition.

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: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:558f20ce899bbb131d8bd67f42d50ac6CVF · CVPR 2026

Thinking Diffusion: Penalize and Guide Visual-Grounded Reasoning in Diffusion Multimodal Language Models

Kim, Keuntae, Kang, Mingyu, Choi, Yong Suk

原文摘要

Diffusion large language models (dLLMs) are emerging as promising alternatives to autoregressive (AR) LLMs. Recently, this paradigm has been extended to multimodal tasks, leading to the development of diffusion multimodal large language models (dMLLMs). These models are expected to retain the reasoning capabilities of LLMs while enabling faster inference through parallel generation. However, when combined with Chain-of-Thought (CoT) reasoning, dMLLMs exhibit two critical issues.First, we observe that dMLLMs often generate the final answer token at a very early timestep. This trend indicates that the model determines the answer before sufficient reasoning, leading to degraded reasoning performance. Second, during the initial timesteps, dMLLMs show minimal dependency on visual prompts, exhibiting a fundamentally different pattern of visual information utilization compared to AR vision-language models. In summary, these findings indicate that dMLLMs tend to generate premature final answers without sufficiently grounding on visual inputs.To address these limitations, we propose Position & Step Penalty (PSP) and Visual Reasoning Guidance (VRG). PSP penalizes tokens in later positions during early timesteps, delaying premature answer generation and encouraging progressive reasoning across timesteps. VRG, inspired by the classifier-free guidance, amplifies visual grounding signals to enhance the model's alignment with visual evidence.Extensive experiments across various dMLLMs demonstrate that our method achieves up to 7.5% higher accuracy while delivering more than 3x speedup compared to reasoning with four times more diffusion steps. Our code will be released after publication.

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:55924512b5692421603e1f3d8f9d7e03CVF · CVPR 2026

MindDriver: Introducing Progressive Multimodal Reasoning for Autonomous Driving

Zhang, Lingjun, Yuan, Yujian, Wu, Changjie, Chang, Xinyuan, Cai, Xin, Zeng, Shuang, Shi, Linzhe, Wang, Sijin 等 10 位作者

原文摘要

Vision-Language Models (VLM) exhibit strong reasoning capabilities, showing promise for end-to-end autonomous driving systems. Chain-of-Thought (CoT), as VLM's widely used reasoning strategy, is facing critical challenges. Existing textual CoT has a large gap between text semantic space and trajectory physical space. Although the recent approach utilizes future image to replace text as CoT process, it lacks clear planning-oriented objective guidance to generate images with accurate scene evolution. To address these, we innovatively propose MindDriver, a progressive multimodal reasoning framework that enables VLM to imitate human-like progressive thinking for autonomous driving.MindDriver presents semantic understanding, semantic-to-physical space imagination, and physical-space trajectory planning.To achieve aligned reasoning processes in MindDriver, we develop a feedback-guided automatic data annotation pipeline to generate aligned multimodal reasoning training data. Furthermore, we develop a progressive reinforcement fine-tuningmethod to optimize the alignment through progressive high-level reward-based learning.MindDriver demonstrates superior performance in both nuScences open-loop and Bench2Drive closed-loop evaluation. Codes: https://github.com/hotdogcheesewhite/MindDriver.

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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Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation

Zhao, Chenxi, Zhu, Chen, Feng, Xiaokun, Hao, Aiming, Zhu, Jiashu, Lei, Jiachen, Wu, Jiahong, Chu, Xiangxiang 等 9 位作者

原文摘要

Few-step generation has been a long-standing goal, with recent one-step generation methods exemplified by MeanFlow achieving remarkable results. Existing research on MeanFlow primarily focuses on class-to-image generation. However, an intuitive yet unexplored direction is to extend the condition from fixed class labels to flexible text inputs, enabling richer content creation. Compared to the limited class labels, text conditions pose greater challenges to the model's understanding capability, necessitating the effective integration of powerful text encoders into the MeanFlow framework. Surprisingly, although incorporating text conditions appears straightforward, we find that integrating powerful LLM-based text encoders using conventional training strategies results in unsatisfactory performance. To uncover the underlying cause, we conduct detailed analyses and reveal that, due to the extremely limited number of refinement steps in the MeanFlow generation, such as only one step, the text feature representations are required to possess sufficiently high discriminability. This also explains why discrete and easily distinguishable class features perform well within the MeanFlow framework. Guided by these insights, we leverage a powerful LLM-based text encoder validated to possess the required semantic properties and adapt the MeanFlow generation process to this framework, resulting in efficient text-conditioned synthesis for the first time. Furthermore, we validate our approach on the widely used diffusion model, demonstrating significant generation performance improvements. We hope this work provides a general and practical reference for future research on text-conditioned MeanFlow generation. The code is available at \supp https://github.com/AMAP-ML/EMF.

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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Hear you are: Teaching LLMs Spatial Reasoning with Vision and Spatial Sound

Ryu, Hyeonggon, Chung, Joon Son, Harwath, David

原文摘要

Many audio-visual learning methods have focused on aligning audio and visual information, either through semantic or temporal correspondence. However, most of these works have utilized monaural audio, which does not contain information about the spatial location of the sound source. In contrast, humans and other animals utilize binaural hearing to perceive this spatial information. Combining spatial sound and visual perception enables powerful high-level reasoning: for example, a person looking for their phone may hear the ringing sound coming from a backpack sitting on a table, and quickly infer that the missing phone is inside the backpack. In this paper, we investigate the problem of Audio-Visual Spatial Reasoning. We design a spatial audio-visual question answering dataset to cover scenarios where semantic correspondence between audio and visual signals is absent but spatial alignment exists, as well as cases with multiple audio-visual semantic correspondences that require spatial reasoning to disambiguate. We propose a model that learns spatial comprehension across the audio and vision modalities by connecting them with a large language model and experimentally demonstrate that spatial sound perception is an essential part of our task.

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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Linking Perception, Confidence and Accuracy in MLLMs

Du, Yuetian, Wang, Yucheng, Zhang, Rongyu, Xu, Zhijie, Yang, Boyu, Kong, Ming, Liu, Jie, Zhu, Qiang

原文摘要

Recent advances in Multi-modal Large Language Models (MLLMs) have predominantly focused on enhancing visual \perception to improve \accuracy. However, a critical question remains unexplored: Do models know when they do not know? Through a probing experiment, we reveal a severe \confidence miscalibration problem in MLLMs. To address this, we propose Confidence-Driven Reinforcement Learning (CDRL), which uses original-noise image pairs and a novel confidence-based reward to enhance perceptual sensitivity and robustly calibrate the model's confidence. Beyond training benefits, calibrated confidence enables more effective test-time scaling as a free lunch. We further propose Confidence-Aware Test-Time Scaling (CA-TTS), which dynamically coordinates Self-Consistency, Self-Reflection, and Visual Self-Check modules guided by confidence signals. An Expert Model acts in multiple roles (e.g., Planner, Critic, Voter) to schedule these modules and provide external verification. Our integrated framework establishes new state-of-the-art results with consistent 8.8% gains across four benchmarks. More ablation studies demonstrate the effectiveness of each module and scaling superiority. Our code will be released after the acception.

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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CLIPoint3D: Language-Grounded Few-Shot Unsupervised 3D Point Cloud Domain Adaptation

Singha, Mainak, Mehrotra, Sarthak, Casari, Paolo, Chaudhuri, Subhasis, Ricci, Elisa, Banerjee, Biplab

原文摘要

Recent vision-language models (VLMs) such as CLIP demonstrate impressive cross-modal reasoning, extending beyond images to 3D perception. Yet, these models remain fragile under domain shifts, especially when adapting from synthetic to real-world point clouds. Conventional 3D domain adaptation approaches rely on heavy trainable encoders, yielding strong accuracy but at the cost of efficiency. We introduce CLIPoint3D, the first framework for few-shot unsupervised 3D point cloud domain adaptation built upon CLIP. Our approach projects 3D samples into multiple depth maps and exploits the frozen CLIP backbone, refined through a knowledge-driven prompt tuning scheme that integrates high-level language priors with geometric cues from a lightweight 3D encoder. To adapt task-specific features effectively, we apply parameter-efficient fine-tuning to CLIP's encoders and design an entropy-guided view sampling strategy for selecting confident projections. Furthermore, an optimal transport-based alignment loss and an uncertainty-aware prototype alignment loss collaboratively bridge source-target distribution gaps while maintaining class separability. Extensive experiments on PointDA-10 and GraspNetPC-10 benchmarks show that CLIPoint3D achieves consistent 3-16% accuracy gains over both CLIP-based and conventional encoder-based baselines. Project page: https://sarthakm320.github.io/CLIPoint3D.

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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Same Attention, Different Truths: Put Logit-Lens over Visual Attention to Detect and Mitigate LVLM Object Hallucination

Wang, Zichuan, Yang, Songlin, Peng, Bo, Tang, Zhenchen, Li, Yang, Dong, Beibei, Dong, Jing

原文摘要

Large Vision-Language Models (LVLMs) often suffer from object hallucination, generating objects that are absent from the image. Prior work largely attributes this to insufficient visual attention. However, in this work, we are surprised to find that both real and hallucinated objects receive equally strong visual attention in the model's mid-to-late layers. This indicates that the key issue may not be how much the model attends, but **what it attends to and why**. To this end, we decode the visual features of high-attention regions using Logit Lens, and observe that high-attention regions corresponding to real objects can be correctly decoded to the target object token, whereas those for hallucinated objects cannot. Building on this, we identify two distinct hallucination mechanisms: **(i) visual uncertainty**, triggered by semantically similar or confusable regions, masking these regions eliminates the hallucination. **(ii) contextual prior**, triggered by strong co-occurrence priors, even when the initially attended region is masked, the hallucination persists and attention drifts to other regions. Based on these findings, we propose a simple yet effective training-free **Detect-Mitigate framework** comprising a Logit-Lens Consistency Check to detect hallucination and targeted remedies: High-Attention Regions Masking (HARM) for visual uncertainty hallucination, and Visual Evidence Enhanced Decoding (VEED) for contextual prior hallucination, which leverages genuine visual evidence to suppress erroneous priors. Our approach achieves state-of-the-art results on multiple hallucination 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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MedGRPO: Multi-Task Reinforcement Learning for Heterogeneous Medical Video Understanding

Su, Yuhao, Choudhuri, Anwesa, Gao, Zhongpai, Planche, Benjamin, Nguyen, Van Nguyen, Zheng, Meng, Shen, Yuhan, Innanje, Arun 等 11 位作者

原文摘要

Large vision-language models struggle with medical video understanding, where spatial precision, temporal reasoning, and clinical semantics are critical. To address this, we first introduce MedVidBench, a large-scale benchmark of 531,850 video-instruction pairs across 8 medical sources spanning video, segment, and frame-level tasks, curated through a rigorous quality assurance pipeline with expert-guided prompting and dual-model validation. While supervised fine-tuning on MedVidBench yields noticeable gains, standard Reinforcement Learning (RL) fails due to imbalanced reward scales across datasets, which destabilizes optimization and leads to training collapse. To overcome this, we introduce MedGRPO, a novel RL framework for balanced multi-dataset training with two key innovations: (1) cross-dataset reward normalization that maps each dataset's median performance to a common reward value, ensuring fair optimization regardless of difficulty, and (2) a medical LLM judge that evaluates caption quality on five clinical dimensions through comparative similarity scoring. Supervised fine-tuning Qwen2.5-VL-7B on MedVidBench substantially outperforms GPT-4.1 and Gemini-2.5-Flash across all tasks, demonstrating MedVidBench's efficacy, while our MedGRPO framework further improves upon the SFT baseline across grounding and captioning tasks. Our work establishes a foundational benchmark and robust training methodology for advancing vision-language models in medical domains.

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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Beyond the Global Scores: Fine-Grained Token Grounding as a Robust Detector of LVLM Hallucinations

Nguyen, Tuan Dung, Ho, Minh Khoi, Chen, Qi, Xie, Yutong, Nguyen, Cam-Tu, Nguyen, Minh Khoi, Nguyen, Dang Huy Pham, van den Hengel, Anton 等 11 位作者

原文摘要

Large vision-language models (LVLMs) achieve strong performance on visual reasoning tasks but remain highly susceptible to hallucination. Existing detection methods predominantly rely on coarse, whole-image measures of how an object token relates to the input image. This global strategy is limited: hallucinated tokens may exhibit weak but widely scattered correlations across many local regions, which aggregate into deceptively high overall relevance, thus evading the current global hallucination detectors. We begin with a simple yet critical observation: a faithful object token must be strongly grounded in a specific image region. Building on this insight, we introduce a patch-level hallucination detection framework that examines fine-grained token-level interactions across model layers. Our analysis uncovers two characteristic signatures of hallucinated tokens: (i) they yield diffuse, non-localized attention patterns, in contrast to the compact, well-focused attention and (ii) they fail to exhibit meaningful semantic alignment with any visual region. Guided by these findings, we develop a lightweight and interpretable detection method that leverages patch-level statistical features, combined with hidden-layer representations. Our approach achieves up to 90% accuracy in token-level hallucination detection, demonstrating the superiority of fine-grained structural analysis for detecting hallucinations.

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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S2D: Selective Spectral Decay for Quantization-Friendly Conditioning of Neural Activations

Chavan, Arnav, Lele, Nahush, Bamba, Udbhav, Dayal, Sankalp, Raghunathan, Aditi, Gupta, Deepak

原文摘要

Activation outliers in large-scale transformer models pose a fundamental challenge to model quantization, creating excessively large ranges that cause severe accuracy drops during quantization. We empirically observe that outlier severity intensifies with pre-training scale (e.g., progressing from CLIP to the more extensively trained SigLIP and SigLIP2). Through theoretical analysis as well as empirical correlation studies, we establish the direct link between these activation outliers and dominant singular values of the weights. Building on this insight, we propose Selective Spectral Decay (S2D), a geometrically-principled conditioning method that surgically regularizes only the weight components corresponding to the largest singular values during fine-tuning. Through extensive experiments, we demonstrate that S2D significantly reduces activation outliers and produces well-conditioned representations that are inherently quantization-friendly. Models trained with S2D achieve up to 7% improved PTQ accuracy on ImageNet under W4A4 quantization and 4% gains when combined with QAT. These improvements also generalize across downstream tasks and vision-language models, enabling the scaling of increasingly large and rigorously trained models without sacrificing deployment efficiency.

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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VLM-Loc: Localization in Point Cloud Maps via Vision-Language Models

Kang, Shuhao, Liao, Youqi, Wang, Peijie, Liao, Wenlong, Zhang, Qilin, Busam, Benjamin, Chen, Xieyuanli, Liu, Yun

原文摘要

Text-to-point-cloud (T2P) localization aims to infer precise spatial positions within 3D point cloud maps from natural language descriptions, reflecting how humans perceive and communicate spatial layouts through language. However, existing methods largely rely on shallow text-point cloud correspondence without effective spatial reasoning, limiting their accuracy in complex environments. To address this limitation, we propose VLM-Loc, a framework that leverages the spatial reasoning capability of large vision-language models (VLMs) for T2P localization. Specifically, we transform point clouds into bird's-eye-view (BEV) images and scene graphs that jointly encode geometric and semantic context, providing structured inputs for the VLM to learn cross-modal representations bridging linguistic and spatial semantics. On top of these representations, we introduce a partial node assignment mechanism that explicitly associates textual cues with scene graph nodes, enabling interpretable spatial reasoning for accurate localization. To facilitate systematic evaluation across diverse scenes, we present CityLoc, a benchmark built from multi-source point clouds for fine-grained T2P localization. Experiments on CityLoc demonstrate VLM-Loc achieves superior accuracy and robustness compared to state-of-the-art methods. Our code, model, and dataset are available at \href https://github.com/MCG-NKU/nku-3d-vision repository .

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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FunFact: Building Probabilistic Functional 3D Scene Graphs via Factor-Graph Reasoning

Fu, Zhengyu, Zurbrügg, René, Qu, Kaixian, Pollefeys, Marc, Hutter, Marco, Blum, Hermann, Bauer, Zuria

原文摘要

Recent work in 3D scene understanding is moving beyond purely spatial analysis toward functional scene understanding. However, existing methods often consider functional relationships between object pairs in isolation, failing to capture the scene-wide interdependence that humans use to resolve ambiguity. We introduce FunFact, a framework for constructing probabilistic open-vocabulary functional 3D scene graphs from posed RGB-D images. FunFact first builds an object- and part-centric 3D map and uses foundation models to propose semantically plausible functional relations. These candidates are converted into factor graph variables and constrained by both LLM-derived common-sense priors and geometric priors. This formulation enables joint probabilistic inference over all functional edges and their marginals, yielding substantially better calibrated confidence scores. To benchmark this setting, we introduce FunThor, a synthetic dataset based on AI2-THOR with part-level geometry and rule-based functional annotations. Experiments on SceneFun3D, FunGraph3D, and FunThor show that FunFact improves node and relation discovery recall and significantly reduces calibration error for ambiguous relations, highlighting the benefits of holistic probabilistic modeling for functional scene understanding. See our project page at https://funfact-scenegraph.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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Revisiting Visual Corruptions in LVLMs: A Shape-Texture Perspective on Model Failures

Qiu, Xinkuan, Kan, Meina, He, Zhenliang, Zhou, Yongbin, Shan, Shiguang

原文摘要

Large vision-language models (LVLMs) are highly vulnerable to visual corruptions, substantially compromising their reliability and limiting real-world deployment. Prior work has attributed this degradation primarily to insufficient visual grounding and overreliance on language priors. However, these explanations often overlook the heterogeneous nature of corruptions, which perturb model perception in fundamentally different ways. We revisit this problem from a corruption-centric perspective and show that diverse corruptions can be organized along two complementary perceptual dimensions--shape and texture--which induce distinct failure modes. To address them, we propose Shape-Texture Dual-Path Contrastive Decoding (ST-CD), a training-free inference framework that constructs complementary contrastive views to diagnose and correct shape- and texture-induced biases through adaptive fusion. Experiments across multiple LVLMs and robustness benchmarks demonstrate that ST-CD consistently improves robustness under heterogeneous corruptions, suggesting that leveraging the complementarity between shape and texture provides a general and effective principle for building robust multimodal models.

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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