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首次观测 · 记录 #1CVF · CVPR 2026:cvf:681da26d9b95e72e514580a32272aeb8CVF · CVPR 2026

NavForesee: A Unified Vision-Language World Model for Hierarchical Planning and Dual-Horizon Navigation Prediction

Liu, Fei, Xie, Shichao, Luo, Minghua, Chu, Zedong, Hu, Junjun, Wu, Xiaolong, Xu, Mu

原文摘要

Embodied navigation for long-horizon tasks, guided by complex natural language instructions, remains a formidable challenge in artificial intelligence. Existing agents often struggle with robust long-term planning about unseen environments, leading to high failure rates. To address these limitations, we introduce NavForesee, a novel Vision-Language Model (VLM) that unifies high-level language planning and predictive world model imagination within a single, unified framework.Our approach empowers a single VLM to concurrently perform planning and predictive foresight. Conditioned on the full instruction and historical observations, the model is trained to understand the navigation instructions by decomposing the task, tracking its progress, and formulating the subsequent sub-goal. Simultaneously, it functions as a generative world model, providing crucial foresight by predicting short-term environmental dynamics and long-term navigation milestones. The VLM's structured plan guides its targeted prediction, while the imagined future provides rich context to inform the navigation actions, creating a powerful internal feedback loop of perception-planning/prediction-action. We demonstrate through extensive experiments on the R2R-CE and RxR-CE benchmark that NavForesee achieves highly competitive performance in complex scenarios. Our work highlights the immense potential of fusing explicit language planning with implicit spatiotemporal prediction, paving the way for more intelligent and capable embodied agents.

cs.CV · cs.CL · cs.AI

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:683ae53df04f7e5ad32862c9401b7e1dCVF · CVPR 2026

Understanding Counting Mechanisms in Large Language and Vision-Language Models

Hasani, Hosein, Izadi, Amirmohammad, Askari, Fatemeh, Bagherian, Mobin, Mohammadian, Sadegh, Izadi, Mohammad, Baghshah, Mahdieh Soleymani

原文摘要

Counting is one of the fundamental abilities of large language models (LLMs) and large vision-language models (LVLMs). This paper examines how these foundation models represent and compute numerical information in counting tasks. We use controlled experiments with repeated textual and visual items and analyze counting in LLMs and LVLMs through a set of behavioral, observational, and causal mediation analyses. To this end, we design a specialized tool, CountScope, for the mechanistic interpretability of numerical content. Results show that individual tokens or visual features encode latent positional count information that can be extracted and transferred across contexts. Layerwise analyses reveal a progressive emergence of numerical representations, with lower layers encoding small counts and higher layers representing larger ones. We identify an internal counter mechanism that updates with each item, stored mainly in the final token or region. In LVLMs, numerical information also appears in visual embeddings, shifting between background and foreground regions depending on spatial composition. We further reveal that models rely on structural cues such as separators in text, which act as shortcuts for tracking item counts and strongly influence the accuracy of numerical predictions. Overall, counting emerges as a structured, layerwise process in LLMs and follows the same general pattern in LVLMs, shaped by the properties of the vision encoder.

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:68afb394e431bf35e96ee8e9c76c0a02CVF · CVPR 2026

Flat-Pack Bench: Evaluating Spatio-Temporal Understanding in Large Vision-Language Models through Furniture Assembly

Chetan, Aditya, Cai, Eric, Kushwaha, Peeyush, Kani, Bharath Raj Nagoor, Mall, Utkarsh, Wang, Qianqian, Snavely, Noah, Hariharan, Bharath

原文摘要

The emergence of Large Vision-Language Models (LVLMs) has significantly advanced video understanding capabilities. However, existing benchmarks focus predominantly on coarse-grained tasks such as action segmentation, classification, captioning, and retrieval. Furthermore, these benchmarks often rely on entities that can be easily identified verbally, like household objects, animals, human subjects, etc., limiting their applicability to complex, in-the-wild video scenarios. But, many applications such as furniture assembly, cooking, etc., require step-by-step fine-grained spatio-temporal understanding of the video, which is not sufficiently evaluated in current benchmarks.To address this gap, we introduce Flat-Pack Bench, a novel benchmark centered on furniture assembly tasks. Our benchmark evaluates LVLMs on nuanced tasks, including temporal ordering of assembly actions, temporal localization of assembly state, understanding part mating, and tracking, using multiple-choice questions paired with visual prompts highlighting relevant parts as references for fine-grained questions. Our experiments reveal that state-of-the-art LVLMs struggle significantly with fine-grained spatio-temporal reasoning, highlighting their limitations in effectively leveraging temporal information from videos, limited tracking ability, and understanding of spatial interactions like physical contact.

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:68bc91a9c5ba0f053292812ef3ae70ffCVF · CVPR 2026

GeoViS: Geospatially Rewarded Visual Search for Remote Sensing Visual Grounding

Zhang, Peirong, Zhang, Yidan, Xu, Luxiao, Lin, Jinliang, Guo, Zonghao, Wang, Fengxiang, Yang, Xue, Wei, Kaiwen 等 9 位作者

原文摘要

Recent advances in multimodal large language models (MLLMs) have led to remarkable progress in visual grounding, enabling fine-grained cross-modal alignment between textual queries and image regions. However, transferring such capabilities to remote sensing imagery remains challenging, as targets are often extremely small within kilometer-scale scenes, and queries typically involve intricate geospatial relations such as relative positions, spatial hierarchies, or contextual dependencies across distant objects.To address these challenges, we propose GeoViS, a Geospatially Rewarded Visual Search framework that reformulates remote sensing visual grounding as a progressive search-and-reasoning process. Rather than directly predicting the target location in a single step, GeoViS actively explores the global image through a tree-structured sequence of visual cues, integrating multimodal perception, spatial reasoning, and reward-guided exploration to refine geospatial hypotheses iteratively. This design enables the model to detect subtle small-scale targets while maintaining holistic scene awareness.Extensive experiments on five remote sensing grounding benchmarks demonstrate that GeoViS achieves precise geospatial understanding and consistently surpasses existing methods across key visual grounding metrics, highlighting its strong cross-domain generalization and interpretability.

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:68c540bfc2c08b3aa89a9c527d2597d0CVF · CVPR 2026

Multimodal Causality-Driven Representation Learning for Generalizable Medical Image Segmentation

Liang, Xusheng, Zhou, Lihua, Li, Nianxin, Xu, Miao, Song, Ziyang, Yi, Dong, Wu, Jinlin, Ma, Jiawei 等 11 位作者

原文摘要

Vision-Language Models (VLMs), such as CLIP, have demonstrated remarkable zero-shot capabilities in various computer vision tasks. However, their application to medical imaging remains challenging due to the high variability and complexity of medical data. Specifically, medical images often exhibit significant domain shifts caused by various confounders, including equipment differences, procedure artifacts, and imaging modes, which can lead to poor generalization when models are applied to unseen domains. To address this limitation, we propose Multimodal Causal-Driven Representation Learning (MCDRL), a novel framework that integrates causal inference with the VLM to tackle domain generalization in medical image segmentation. MCDRL is implemented in two steps: first, it leverages CLIP's cross-modal capabilities to identify candidate lesion regions and construct a confounder dictionary through text prompts, specifically designed to represent domain-specific variations; second, it trains a causal intervention network that utilizes this dictionary to identify and eliminate the influence of these domain-specific variations while preserving the anatomical structural information critical for segmentation tasks. Extensive experiments demonstrate that MCDRL consistently outperforms competing methods, yielding superior segmentation accuracy and exhibiting robust generalizability.

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:68c8143ce05ea22bdce2d18296bb7264CVF · CVPR 2026

SkeletonContext: Skeleton-side Context Prompt Learning for Zero-Shot Skeleton-based Action Recognition

Wang, Ning, Wu, Tieyue, Sharif, Naeha, Boussaid, Farid, Zhu, Guangming, Mei, Lin, Bennamoun, Mohammed, Zhang, Liang

原文摘要

Zero-shot skeleton-based action recognition aims to recognize unseen actions by transferring knowledge from seen categories through semantic descriptions. Most existing methods typically align skeleton features with textual embeddings within a shared latent space. However, the absence of contextual cues, such as objects involved in the action, introduces an inherent gap between skeleton and semantic representations, making it difficult to distinguish visually similar actions. To address this, we propose SkeletonContext, a prompt-based framework that enriches skeletal motion representations with language-driven contextual semantics. Specifically, we introduce a Cross-Modal Context Prompt Module, which leverages a pretrained language model to reconstruct masked contextual prompts under guidance derived from LLMs. This design effectively transfers linguistic context to the skeleton encoder for instance-level semantic grounding and improved cross-modal alignment. In addition, a Key-Part Decoupling Module is incorporated to decouple motion-relevant joint features, ensuring robust action understanding even in the absence of explicit object interactions. Extensive experiments on multiple benchmarks demonstrate that SkeletonContext achieves state-of-the-art performance under both conventional and generalized zero-shot settings, validating its effectiveness in reasoning about context and distinguishing fine-grained, visually similar actions. Our project is available at:https://github.com/NingWang2049/skeletoncontext.

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:68cb53b093cd1ee229d2e62e7a40747eCVF · CVPR 2026

FARMER: Flow AutoRegressive Transformer over Pixels

Zheng, Guangting, Zhao, Qinyu, Yang, Tao, Xiao, Fei, Lin, Zhijie, Wu, Jie, Deng, Jiajun, Zhang, Yanyong 等 9 位作者

原文摘要

Directly modeling the explicit likelihood of the raw data distribution is a key topic in the machine learning area, which achieves the scaling success in Large Language Models by autoregressive modeling. However, continuous AR modeling over visual pixel data suffers from extremely long sequences and high-dimensional spaces. In this paper, we present FARMER, a novel end-to-end generative framework that unifies Normalizing Flows (NFs) and Autoregressive (AR) models for tractable likelihood estimation and high-quality image synthesis directly from raw pixels. FARMER employs an invertible autoregressive flow to transform images into latent sequences, whose distribution is implicitly modeled by an autoregressive model. To address the redundancy and complexity in pixel-level modeling, we propose a self-supervised dimension reduction scheme that partitions NF latent channels into informative and redundant groups, enabling more effective and efficient AR modeling. Furthermore, we design a one-step distillation scheme to significantly accelerate inference speed and introduce a resampling-based classifier-free guidance algorithm to boost image generation quality. Extensive experiments demonstrate that FARMER achieves competitive performance compared to existing pixel-based generative models while providing exact likelihoods and scalable 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:68ef187a39580c8c44fee738f7dcb151CVF · CVPR 2026

SEA: Evaluating Sketch Abstraction Efficiency via Element-level Commonsense Visual Question Answering

Park, Jiho, Choi, Sieun, Seo, Jaeyoon, Sohn, Minho, Kim, Yeana, Kim, Jihie

原文摘要

A sketch is a distilled form of visual abstraction that conveys core concepts through simplified yet purposeful strokes while omitting extraneous detail. Despite its expressive power, quantifying the efficiency of semantic abstraction in sketches remains challenging. Existing evaluation methods that rely on reference images, low-level visual features, or recognition accuracy do not capture abstraction, the defining property of sketches.To address these limitations, we introduce SEA (Sketch Evaluation metric for Abstraction efficiency), a reference-free metric that assesses how economically a sketch represents class-defining visual elements while preserving semantic recognizability. These elements are derived per class from commonsense knowledge about features typically depicted in sketches. SEA leverages a visual question answering model to determine the presence of each element and returns a quantitative score that reflects semantic retention under simple visual representations.To support this metric, we present CommonSketch, the first semantically annotated sketch dataset, comprising 23,100 human-drawn sketches across 300 classes, each paired with a caption and element-level annotations. Experiments show that SEA aligns closely with human judgments and reliably discriminates levels of abstraction efficiency, while CommonSketch serves as a benchmark providing systematic evaluation of element-level sketch understanding across various vision-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:6908305af85ae6cac1e90fc8e3b06cc1CVF · CVPR 2026

AceTone: Bridging Words and Colors for Conditional Image Grading

Ma, Tianren, Liao, Mingxiang, Zhang, Xijin, Ye, Qixiang

原文摘要

Color affects how we interpret image style and emotion. Previous color grading methods rely on patch-wise recoloring or fixed filter banks, struggling to generalize across creative intents or align with human aesthetic preferences. In this study, we propose **AceTone**, the first approach that supports multimodal conditioned color grading within a unified framework. AceTone formulates grading as a generative color transformation task, where a model directly produces 3D-LUTs conditioned on text prompts or reference images. We develop a VQ-VAE-based tokenizer which compresses a 3x32^3 LUT vector to 64 discrete tokens with \Delta \text E <2 fidelity. We further build a large-scale dataset, AceTone-800K, and train a vision-language model to predict LUT tokens, followed by reinforcement learning to align outputs with perceptual fidelity and aesthetics. Experiments show that AceTone achieves state-of-the-art performance on both text-guided and reference-guided grading tasks, improving LPIPS by up to **50%** over existing methods. Human evaluations confirm that AceTone's results are visually pleasing and stylistically coherent, demonstrating a new pathway toward language-driven, aesthetic-aligned color grading. Project Page: github.com/martian422/AceTone.

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:69416f9f04c6671625f1824b11b48f5cCVF · CVPR 2026

Dr. Seg: Revisiting GRPO Training for Visual Large Language Models through Perception-Oriented Design

Sun, Haoxiang, Wang, Tao, Tang, Chenwei, Yuan, Li, Lv, Jiancheng

原文摘要

Following the success of Group Relative Policy Optimization (GRPO) in foundation LLMs, an increasing number of works have sought to adapt GRPO to Visual Large Language Models (VLLMs) for visual perception tasks (e.g., detection and segmentation). However, much of this line of research rests on a long-standing yet unexamined assumption: training paradigms developed for language reasoning can be transferred seamlessly to visual perception. Our experiments show that this assumption is not valid, revealing intrinsic differences between reasoning-oriented and perception-oriented settings. Using reasoning segmentation as a representative case, we surface two overlooked factors: (i) the need for a broader output space, and (ii) the importance of fine-grained, stable rewards. Building on these observations, we propose Dr.Seg, a simple, plug-and-play GRPO-based framework consisting of a Look-to-Confirm mechanism and a Distribution-Ranked Reward module, requiring no architectural modifications and integrating seamlessly with existing GRPO-based VLLMs. Extensive experiments demonstrate that Dr.Seg improves performance in complex visual scenarios while maintaining strong generalization. Code, data, and models will be publicly released 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:699a56917af540a4d1ecdd3b3b2f1c53CVF · CVPR 2026

SMAP: Semantic Route Planning with Map-Grounded Multimodal Alignment

Zhang, Wenjie, Yang, Chen, Lu, Xin, Wang, Zhen, Liu, Yue, Xi, Bobo, Zhang, Pengbo

原文摘要

Semantic route planning involves generating itineraries that align with user intent while respecting real-world spatial constraints. However, text-only large language models (LLMs) often hallucinate geographically implausible routes due to poor spatial grounding. Inspired by how humans use maps for route planning, we propose the SMAP, which is the first multimodal framework combining user queries, POI metadata, and map tiles to produce spatially coherent, preference-aware routes. To enhance the spatial consistency, the SMAP features a two-stage anti-hallucination mechanism: (1) a map-grounded self-editing pipeline where a multimodal LLM (MLLM) drafts routes and a second MLLM verifies and refines them using geographic evidence; and (2) hallucination-penalized Direct Preference Optimization (HDPO) that steers the route generator toward spatially plausible routes by using verified routes as accepted responses and hallucinated drafts as rejected ones. Additionally, we introduce MM-Route, the first multimodal dataset for semantic route planning, with 3,000 diverse queries annotated with POI metadata and map tiles, covering a broad spectrum of geographic granularities and user intents. Experimental results demonstrate that SMAP significantly reduces geographical hallucinations and outperforms strong baselines in spatial plausibility and user alignment. Our project is at https://amap-mobility-intelligence.github.io/SMAP/.

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:69b6d849eb069fdff45bc61799b1d18cCVF · CVPR 2026

VOLD: Reasoning Transfer from LLMs to Vision-Language Models via On-Policy Distillation

Bousselham, Walid, Kuehne, Hilde, Schmid, Cordelia

原文摘要

Training vision-language models (VLMs) for complex reasoning remains a challenging task, i.a. due to the scarcity of high-quality image-text reasoning data. Conversely, text-based reasoning resources are abundant and scalable, but it is still an open question how to leveraging them for VLM reasoning. To address this problem, we propose VOLD, a framework to transfer reasoning capabilities from text-only teacher models to VLM student models. To this end, VOLD combines reinforcement learning via Group Relative Policy Optimization (GRPO) with on-policy distillation, which allows the student reasoning traces to be guided by the teacher model, resulting in a significant gain over using GRPO alone.We further show that a cold-start alignment is essential for an effective transfer during the online training phase in this scenario and that without sufficient distributional alignment between teacher and student, on-policy distillation fails to provide meaningful guidance.We evaluate VOLD across diverse benchmarks including MMMU-Pro, MathVision, MathVista, and LogicVista, showing that VOLD outperforms the baseline model significantly and improves over the state of the art by a margin.Our ablation shows the importance of a cold-start alignment via SFT for on-policy distillation with a text-only teacher

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:69c2f7e8beec6d002b37f0977596525bCVF · CVPR 2026

Graph2Eval: Automatic Multimodal Task Generation for Agents via Knowledge Graphs

Chen, Yurun, Hu, Xueyu, Liu, Yuhan, Wang, Ziqi, Liao, Zeyi, Chen, Lin, Wei, Feng, Qian, Yuxi 等 11 位作者

原文摘要

As multimodal LLM-driven agents advance in autonomy and generalization, traditional static datasets face inherent scalability limitations and are insufficient for fully assessing their capabilities in increasingly complex and diverse tasks. Existing studies have attempted to generate agent tasks using LLMs, but due to the inherent hallucinations of LLMs and the lack of internal data relationship modeling, these tasks often exhibit semantic inconsistencies and solvability issues. To address these challenges, we introduce Graph2Eval, a knowledge-graph-driven framework for automated, scalable, and semantically grounded agent task generation. At its core, Graph2Eval leverages a knowledge graph built from heterogeneous external data sources as a structured task space, generating multimodal agent tasks through subgraph sampling and task construction guided by task templates and meta-path strategies. To further ensure task reliability, a multi-stage filtering pipeline based on node reachability analysis, LLM scoring, and similarity analysis ensures the diversity and solvability of the generated tasks. By unifying both RAG Agent and Web Agent scenarios, Graph2Eval enables efficient generation of multimodal document understanding tasks and multi-step web interaction tasks. We instantiate the framework with Graph2Eval-Bench, a curated dataset of 1,319 tasks spanning document understanding and web interaction scenarios. Extensive experiments show that, on average, Graph2Eval improves task semantic consistency by 20% and solvability by 17% over baselines, while Graph2Eval-Bench effectively distinguishes agent performance, offering a new perspective on automated agent evaluation.

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:69cb121cdb87257c1c537c927d1d7553CVF · CVPR 2026

Logit-Margin Repulsion for Backdoor Defense

Yang, Zhiguo, Xu, Dongsheng, Zhong, Ruizhi, Pi, Jiacheng, Huang, Xingxing, Ruan, Wenjie

原文摘要

Backdoor attacks pose a significant threat to deep neural networks. Recent studies have shown that model compression, such as quantization and pruning, can be exploited by attackers to implant conditional backdoors. Such backdoors remain dormant in the original model but are activated after the model undergoes specific operations, making them highly stealthy and difficult to detect. Traditional defense methods struggle to counter this type of attack, while defenses specifically designed for conditional backdoors also have difficulty handling traditional backdoor attacks. To address these challenges, we propose a universal defense method, termed Logit Margin Repulsion (LMR). LMR uses a small set of clean samples and combines selective cross-entropy with a logit-margin constraint to enlarge the gap between the backdoor class and benign classes. It then removes channels associated with backdoor behavior through selective pruning, thereby achieving strong backdoor purification. Extensive experiments on a variety of CNNs and Vision Transformers demonstrate that, even with an extremely limited amount of clean data (0.1%), LMR can effectively mitigate both traditional and conditional backdoor attacks. The implementation is publicly available on https://github.com/Trusted-LLM/LMR.

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:69d3d34a006ad70f26e008708f220aadCVF · CVPR 2026

3D Space as a Scratchpad for Editable Text-to-Image Generation

Saha, Oindrila, Krs, Vojtech, Mech, Radomir, Maji, Subhransu, Gadelha, Matheus, Blackburn-Matzen, Kevin

原文摘要

Recent progress in large language models (LLMs) has shown that reasoning improves when intermediate thoughts are externalized into explicit workspaces, such as chain-of-thought traces or tool-augmented reasoning. Yet, visual language models (VLMs) lack an analogous mechanism for spatial reasoning, limiting their ability to generate images that accurately reflect geometric relations, object identities, and compositional intent. We introduce the concept of a spatial scratchpad -- a 3D reasoning substrate that bridges linguistic intent and image synthesis. Given a text prompt, our framework parses subjects and background elements, instantiates them as editable 3D meshes, and employs agentic scene planning for placement, orientation, and viewpoint selection. The resulting 3D arrangement is rendered back into the image domain with identity-preserving cues, enabling the VLM to generate spatially consistent and visually coherent outputs. Unlike prior 2D layout-based methods, our approach supports intuitive 3D edits that propagate reliably into final images. Empirically, it achieves a 32% improvement in text alignment on GenAI-Bench, demonstrating the benefit of explicit 3D reasoning for precise, controllable image generation. Our results highlight a new paradigm for vision-language models that deliberate not only in language, but also in space. Code and visualizations at https://oindrilasaha.github.io/3DScratchpad/

cs.CV · cs.CL · cs.AI

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6a178e621ca6da84e1ecdfd88de50fa4CVF · CVPR 2026

UNI-OOD: Unified Object- and Image-level Out-of-Distribution Detection via Cross-Context Attentive Vision-Language Modeling

Li, Yuchuan, Motamedi, Azadeh, Kwon, Hyock Ju, Park, Chul B, Kim, Il-Min

原文摘要

Out-of-distribution (OOD) detection is a key requirement for reliable deployment in open-world environments, where a model must recognize inputs that fall outside the semantic scope of known concepts. While recent advances in vision-language models (VLMs) have achieved strong results in image-level OOD detection, most methods still assume that each image contains a single dominant object. This assumption severely limits their applicability to real-world settings where scenes are naturally composed of multiple objects that each demand independent OOD assessment. Existing object-level approaches, including the current SOTA method RUNA, remain constrained by coarse global representations and insufficient modeling of contextual dependencies between objects and their backgrounds. We propose UNI-OOD, a unified framework that performs both object- and image-level OOD detection within a single VLM, without requiring prior knowledge of which task is being addressed at inference time. The key idea is to leverage cross-context attentive modeling that captures complementary visual and textual semantics. UNI-OOD learns to attend to fine-grained spatial details within each object, aligns visual and linguistic embeddings to strengthen semantic correspondence, and models interactions between target objects and their surrounding context. By jointly reasoning over object-centric and background cues, the framework disentangles informative visual evidence from spurious correlations and enables a consistent OOD scoring mechanism across different visual granularities. Extensive experiments on object- and image-level benchmarks demonstrate that UNI-OOD achieves substantial and consistent improvements over previous approaches, establishing new SOTA performance in both object-level and image-level OOD detection.

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:6a1fc2c5c546c8ad97da2f7c0f52b7d1CVF · CVPR 2026

Forging a Dynamic Memory: Retrieval-Guided Continual Learning for Generalist Medical Foundation Models

Chen, Zizhi, Gao, Yizhen, Han, Minghao, Liu, Yizhou, Chen, Zhaoyu, Yang, Dingkang, Zhang, Lihua

原文摘要

Multimodal biomedical Vision-Language Models (VLMs) exhibit immense potential in the field of Continual Learning (CL). However, they confront a core dilemma: how to preserve fine-grained intra-modality features while bridging the significant domain gap across different modalities. To address this challenge, we propose a comprehensive framework. Leveraging our 18-million multimodal and comprehensive medical retrieval database derived from PubMed scientific papers, we pioneer the integration of Retrieval-Augmented Generation (RAG) into CL. Specifically, we employ a multi-modal, multi-layer RAG system that provides real-time guidance for model fine-tuning through dynamic, on-demand knowledge retrieval. Building upon this, we introduce a dynamic knowledge distillation framework. This framework precisely resolves the aforementioned core dilemma by dynamically modulating the importance of the parameter space, the granularity of the distilled knowledge, and the data distribution of the reference dataset in accordance with the required level of detail. To thoroughly validate the clinical value of our strategy, we have designed a more rigorous Medical Generalist Task Incremental Learning (MGTIL) benchmark. This benchmark is engineered to simultaneously evaluate the model's capacity for adaptation to significant domain shifts, retention of subtle intra-domain features, and real-time learning of novel and complex medical tasks. Our method achieves stable SOTA results across all metrics, with https://github.com/CZZZZZZZZZZZZZZZZZ/PRIMED code available.

cs.CV · cs.CL

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

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Chain-of-Thought Guided Multi-Modal Object Re-Identification

Gao, Ya, Li, Shihao, Liu, Zhaojun, Zheng, Aihua, Li, Chenglong, Tang, Jin

原文摘要

With the rise of visual-language models, multi-modal ReID retrieves specific targets by integrating different spectra and textual descriptions. Existing methods merely adopt descriptive representation learning for image-text, ignoring the relationships among the intrinsic logical hierarchies of semantic features. Since Chain-of-Thought (CoT) can provide textual logical context and enhance semantic perception in large-model reasoning, we propose CoT-ReID, a CoT-guided framework that injects the Multi-modal Large Language Models (MLLMs) reasoning into multi-modal ReID. Specifically, we simulate the joint visual-textual logical decision-making of human reasoning, leveraging CoT textual logical reasoning to guide visual feature learning at the early, late, and decision-making level: At the early level, we embed the semantic reversion of CoT hierarchical reasoning into visual features to calibrate bottom-level features and emphasize visual hierarchical reasoning. Next, we take CoT hierarchical reasoning text as an anchor condition to constrain the consistency of visual cross-modal semantics. Finally, through the hierarchical reasoning process of CoT, we embed logically reasoned text attribute features into multi-modal decision-making, providing logical support for selecting discriminative identity features. By constructing CoT textual benchmarks and our proposed modules, our framework generates more robust multi-modal features in complex scenarios. Comprehensive experiments on four datasets (RGBNT100, MSVR310, WMVeID863, RGBNT201) demonstrate that our method outperforms existing approaches. Code will be released 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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MLLM-HWSI: A Multimodal Large Language Model for Hierarchical Whole Slide Image Understanding

Alawode, Basit, Mahmood, Arif, Al Radi, Muaz Khalifa, Albastaki, Shahad, Khan, Asim, Bilal, Muhammad, Abdalla, Moshira Ali, Bennamoun, Mohammed 等 9 位作者

原文摘要

Whole Slide Images (WSIs) exhibit hierarchical structure, where diagnostic information emerges from cellular morphology, regional tissue organization, and global context. Existing Computational Pathology (CPath) Multimodal Large Language Models (MLLMs) typically compress an entire WSI into a single embedding, which hinders fine-grained grounding and ignores how pathologists synthesize evidence across different scales. We introduce MLLM-HWSI, a Hierarchical WSI-level MLLM that aligns visual features with pathology language at four distinct scales, cell as word, patch as phrase, region as sentence, and WSI as paragraph to support interpretable evidence-grounded reasoning. MLLM-HWSI decomposes each WSI into multi-scale embeddings with scale-specific projectors and jointly enforces (i) a hierarchical contrastive objective and (ii) a cross-scale consistency loss, preserving semantic coherence from cells to the WSI. We compute diagnostically relevant patches and aggregate segmented cell embeddings into a compact cellular token per-patch using a lightweight Cell-Cell Attention Fusion (CCAF) transformer. The projected multi-scale tokens are fused with text tokens and fed to an instruction-tuned LLM for open-ended reasoning, VQA, report, and caption generation tasks. Trained in three stages, MLLM-HWSI achieves new SOTA results on 13 WSI-level benchmarks across six CPath tasks. By aligning language with multi-scale visual evidence, MLLM-HWSI provides accurate, interpretable outputs that mirror diagnostic workflows and advance holistic WSI understanding. Code is available at: \href https://github.com/BasitAlawode/HWSI-MLLM Github .

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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Vocabulary Scaling Law: Tuning Open-vocabulary Predictors for Their Openness

Chen, Ziliang, Li, Yulu, Fang, Liangda, Zhang, Jusheng, Zheng, Yongsen, Guan, Quanlong, Chen, Xipeng

原文摘要

Open-vocabulary learning on CLIP provides remarkable generalization on diverse concepts, however, falters under the realistic streaming open-world evaluations for Stability against distractor classes and Extensibility to novel classes. Current fine-tuning methods often fail these tests since they are mainly designed for closed-set conditions, leading to the performance gaps while the target vocabulary progressively scales. We formalize a "vocabulary scaling law" showing that these openness measures can be lower-bounded by performance on the full class-name universe, implying that robust fine-tuning should: (i) account for the entire vocabulary, (ii) tune class-name embeddings rather than context, and (iii) enforce orthogonality between prompt embeddings including training and open-set class names. Guided by our analysis, we propose Submodular-Vocabulary Fine-tuning (SVFT), a bi-level optimization framework that approximates the intractable objective of tuning all class name embedding by greedily selecting a small, informative subset of class names via constrained submodular maximization, thus, allows the employment of efficient greedy algorithm for the near-optimal class-name subset selection to fine-tune CLIP instead of using all open classes. Across extensive experiments, SVFT consistently improves both stability and extensibility, advancing the openness and practical robustness of CLIP-based vision-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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HERBench: A Benchmark for Multi-Evidence Integration in Video Question Answering

Ben Ami, Dan, Serussi, Gabriele, Cohen, Kobi, Baskin, Chaim

原文摘要

Video Large Language Models (Video-LLMs) are improving rapidly, yet current Video Question Answering (VideoQA) benchmarks often admit single-cue shortcuts, under-testing reasoning that must integrate evidence across time. We introduce HERBench, a benchmark designed to make multi-evidence integration unavoidable: each question requires at least three non-overlapping cues drawn from distinct video segments. HERBench contains 26,806 five-way multiple-choice questions across 12 compositional tasks. To make evidential demand measurable, we introduce the Minimum Required Frame-Set (MRFS), the smallest number of frames a model must fuse to answer correctly, and show that HERBench imposes higher evidential demand than prior benchmarks. Evaluating 13 state-of-the-art Video-LLMs yields only 31-42% accuracy, only modestly above the 20% random-guess baseline. We disentangle this failure into two critical bottlenecks: (1) a retrieval deficit, where frame selectors overlook key evidence, and (2) a fusion deficit, where models fail to integrate information even when all necessary evidence is provided. HERBench thus provides a principled benchmark for studying robust multi-evidence video understanding.

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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Narrative Weaver: Towards Controllable Long-Range Visual Consistency with Multi-Modal Conditioning

Yao, Zhengjian, Li, Yongzhi, Gao, Xinyuan, Chen, Quan, Jiang, Peng, Lu, Yanye

原文摘要

We present Narrative Weaver, a novel framework that addresses a fundamental challenge in generative AI: achieving controllable, long-range, and consistent visual content generation. While existing models excel at generating high-fidelity short-form visual content, they struggle to maintain narrative coherence and visual consistency across extended sequences--a critical limitation for real-world applications such as filmmaking and e-commerce advertising.Narrative Weaver introduces the first holistic solution that seamlessly integrates three essential capabilities: fine-grained control, automatic narrative planning, and long-range coherence. Our architecture combines a Multimodal Large Language Model (MLLM) for high-level narrative planning with a novel fine-grained control module featuring a dynamic Memory Bank that prevents visual drift.To enable practical deployment, we develop a progressive, multi-stage training strategy that efficiently leverages existing pre-trained models, achieving state-of-the-art performance even with limited training data. Recognizing the absence of suitable evaluation benchmarks, we construct and release the E-commerce Advertising Video Storyboard Dataset (EAVSD)--the first comprehensive dataset for this task, containing over 330K high-quality images with rich narrative annotations.Through extensive experiments across three distinct scenarios (controllable multi-scene generation, autonomous storytelling, and e-commerce advertising), we demonstrate our method's superiority while opening new possibilities for AI-driven content creation.

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 Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter

Jiang, Bo, Ze, Xueyang, Wang, Beibei, Wang, Xixi, Wan, Xixi, Luo, Bin

原文摘要

Textual adapter-based tuning methods have shown significant potential in transferring knowledge from pre-trained Vision-Language Models (VLMs) to downstream tasks. Existing works generally employ the deterministic textual feature adapter to refine each category textual representation. However, due to inherent factors such as different attributes and contexts, there exists significant diversity in textual descriptions for each category. Such description diversity offers rich discriminative semantic knowledge that can benefit downstream visual learning tasks. Obviously, the traditional deterministic adapter model cannot adequately capture this varied semantic information. Also, it is desirable to exploit the inter-class relationships in VLM adapter. To address these issues, we propose to exploit random graph model into VLM adapter and develop a novel Vertex Random Graph Adapter (VRGAdapter). VRGAdapter first models the inherent diverse descriptions of each category and inter-class relationships of different categories simultaneously by leveraging a Vertex Random Knowledge Graph (VRKG) model. Then, it employs probabilistic message propagation on VRKG to learn context-aware distribution representation for each class node. Finally, it adopts a reparameterized sampling function to achieve textual adapter learning. Note that, VRGAdapter provides a more general adapter solution that encompasses traditional graph-based adapter as a special case. In addition, to enable more robust performance for downstream tasks, we also introduce a new Uncertainty-guided Multi-branch Fusion (UMF) scheme that dynamically integrates multiple pre-trained models for ensemble prediction. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of our approach.

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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Decompose and Transfer: CoT-Prompting Enhanced Alignment for Open-Vocabulary Temporal Action Detection

Zhu, Sa, Zhang, Wanqian, Wang, Lin, Chen, Xiaohua, Cui, Chenxu, Zhang, Jinchao, Li, Bo

原文摘要

Open-Vocabulary Temporal Action Detection (OV-TAD) aims to classify and localize action segments in untrimmed videos for unseen categories. Previous methods rely solely on global alignment between label-level semantics and visual features, which is insufficient to transfer temporal consistent visual knowledge from seen to unseen classes. To address this, we propose a Phase-wise Decomposition and Alignment (PDA) framework, which enables fine-grained action pattern learning for effective prior knowledge transfer. Specifically, we first introduce the CoT-Prompting Semantic Decomposition (CSD) module, which leverages the chain-of-thought (CoT) reasoning ability of large language models to automatically decompose action labels into coherent phase-level descriptions, emulating human cognitive processes. Then, Text-infused Foreground Filtering (TIF) module is introduced to adaptively filter action-relevant segments for each phase leveraging phase-wise semantic cues, producing semantically aligned visual representations. Furthermore, we propose the Adaptive Phase-wise Alignment (APA) module to perform phase-level visual-textual matching, and adaptively aggregates alignment results across phases for final prediction. This adaptive phase-wise alignment facilitates the capture of transferable action patterns and significantly enhances generalization to unseen actions. Extensive experiments on two OV-TAD benchmarks demonstrated the superiority of the proposed method.

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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Time Blindness: Why Video-Language Models Can't See What Humans Can?

Upadhyay, Ujjwal, Ranjan, Mukul, Shen, Zhiqiang, Elhoseiny, Mohamed

原文摘要

Recent advances in vision-language models (VLMs) have made impressive strides in understanding spatio-temporal relationships in videos. However, when spatial information is obscured, these models struggle to capture purely temporal patterns. We introduce SpookyBench, a benchmark where information is encoded solely in temporal sequences of noise-like frames, mirroring natural phenomena from biological signaling to covert communication. Interestingly, while humans can recognize shapes, text, and patterns in these sequences with over 98% accuracy, state-of-the-art VLMs achieve 0% accuracy. This highlights a critical limitation: an over-reliance on frame-level spatial features and an inability to extract meaning from temporal cues. Overcoming this limitation will require novel architectures or training paradigms that decouple spatial dependencies from temporal processing. Our systematic analysis shows that this issue persists across model scales and architectures. We release SpookyBench to catalyze research in temporal pattern recognition and bridge the gap between human and machine video understanding. Dataset and code are available on the project website.

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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Unstitching the Chimera: Frame-Level Risk and Train-Free Mitigation for Video Hallucination

Yang, Songyuan, Tang, Guijian, Hu, Kun, Wang, Haotian, Liu, Shixuan, Yang, Wenjing, Lan, Long, Tan, Huibin

原文摘要

Hallucination limits the reliability of multimodal large language models (MLLMs), and it is particularly damaging in video where errors manifest as distorted narratives rather than single-frame mistakes. We introduce a frame-first study of **Chimera Hallucination**: model stitches visual segments that exist in space and time but do not belong to the same event chain, producing a spurious continuous story. We introduce **CH-Risk**, a single-forward, reference-free risk estimate tailored to this failure mode. CH-Risk combines two complementary signals: SegCoverage@\alpha (\mathrm SCR @\alpha) measures how many event segments are needed to cover most text-to-frame support, exposing long-range stitching; Alignment with Early Temporal Pathway (AETP) measures rank consistency between support and the temporal pathway formed in early-middle layers, exposing stage mismatch. To turn risk into correction, we further propose **CH-M(itigation)**, a train-free two-stage intervention. Segment-aligned Stage-Aligned Frame Routing (sSAFR) re-weights frames before the mid-layer softmax to route attention toward a small set of pathway-aligned segments. Residual Token Calibration (RTC) then stabilizes token usage within selected segments. Extensive experiments across 9 benchmarks and 6 VideoLLMs show that CH-Risk can predict Chimera and that CH-M consistently reduces hallucination and improves task accuracy with negligible overhead (sub-5% latency, sub-2.5% memory, \~1% FLOPs).

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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Venus: Benchmarking and Empowering Multimodal Large Language Models for Aesthetic Guidance and Cropping

Du, Tianxiang, He, Hulingxiao, Peng, Yuxin

原文摘要

The widespread use of smartphones has made photography ubiquitous, yet a clear gap remains between ordinary users and professional photographers, who can identify aesthetic issues and provide actionable shooting guidance during capture. We define this capability as aesthetic guidance (AG) --- an essential but largely underexplored domain in computational aesthetics. Existing multimodal large language models (MLLMs) primarily offer overly positive feedback, failing to identify issues or provide actionable guidance. Without AG capability, they cannot effectively identify distracting regions or optimize compositional balance, thus also struggling in aesthetic cropping, which aims to refine photo composition through reframing after capture. To address this, we introduce AesGuide, the first large-scale AG dataset and benchmark with 10,748 photos annotated with aesthetic scores, analyses, and guidance. Building upon it, we propose Venus, a two-stage framework that first empowers MLLMs with AG capability through progressively complex aesthetic questions and then activates their aesthetic cropping power via CoT-based rationales. Extensive experiments show that Venus substantially improves AG capability and achieves state-of-the-art (SOTA) performance in aesthetic cropping, enabling interpretable and interactive aesthetic refinement across both stages of photo creation. Code is available at https://github.com/PKU-ICST-MIPL/Venus_CVPR2026.

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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IsoCLIP: Decomposing CLIP Projectors for Efficient Intra-modal Alignment

Magistri, Simone, Goswami, Dipam, Mistretta, Marco, Twardowski, Bartłomiej, van de Weijer, Joost, Bagdanov, Andrew D.

原文摘要

Vision-Language Models like CLIP are extensively used for inter-modal tasks which involve both visual and text modalities. However, when the individual modality encoders are applied to inherently intra-modal tasks like image-to-image retrieval, their performance suffers from the intra-modal misalignment. In this paper we study intra-modal misalignment in CLIP with a focus on the role of the projectors that map pre-projection image and text embeddings into the shared embedding space. By analyzing the form of the cosine similarity applied to projected features, and its interaction with the contrastive CLIP loss, we show that there is an inter-modal operator responsible for aligning the two modalities during training, and a second, intra-modal operator that only enforces intra-modal normalization but does nothing to promote intra-modal alignment. Via spectral analysis of the inter-modal operator, we identify an approximately isotropic subspace in which the two modalities are well-aligned, as well as anisotropic directions specific to each modality. We demonstrate that this aligned subspace can be directly obtained from the projector weights and that removing the anisotropic directions improves intra-modal alignment. Our experiments on intra-modal retrieval and classification benchmarks show that our training-free method reduces intra-modal misalignment, greatly lowers latency, and outperforms existing approaches across multiple pre-trained CLIP-like models. The code is publicly available at: https://github.com/simomagi/IsoCLIP.

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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Bidirectional Multimodal Prompt Learning with Scale-Aware Training for Few-Shot Multi-Class Anomaly Detection

Lee, Yujin, Kim, Sewon, Moon, Daeun, Jang, Seoyoon, Yoon, Hyunsoo

原文摘要

Few-shot multi-class anomaly detection is crucial in real industrial settings, where only a few normal samples are available while numerous object types must be inspected. This setting is challenging as defect patterns vary widely across categories while normal samples remain scarce. Existing vision-language model-based approaches typically depend on class-specific anomaly descriptions or auxiliary modules, limiting both scalability and computational efficiency. In this work, we propose AnoPLe, a lightweight multimodal prompt learning framework that removes reliance on anomaly-type textual descriptions and avoids any external modules. AnoPLe employs bidirectional interactions between textual and visual prompts, allowing class semantics and instance-level cues to refine one another and form class-conditioned representations that capture shared normal patterns across categories. To enhance localization, we design a scale-aware prefix trained on both global and local views, enabling the prompts to capture both global context and fine-grained details. In addition, alignment loss propagates local anomaly evidence to global features, strengthening the consistency between pixel- and image-level predictions. Despite its simplicity, AnoPLe achieves strong performance on MVTec-AD, VisA, and Real-IAD under the few-shot multi-class setting, surpassing prior approaches while remaining efficient and free from expert-crafted anomaly descriptions. Moreover, AnoPLe generalizes well to unseen anomalies and extends effectively to the medical domain.

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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FPS-Bench: A Benchmark for High Frame-Rate Video Understanding

Choudhury, Rohan, Dandurand, Jean-Sebastien, Qiu, Kai, Bhat, Kshitij Madhav, Sharma, Kartik, Dahiya, Liza, Zhao, Yizhou, Kundu, Souraja 等 11 位作者

原文摘要

Modern video-language models are typically trained on videos downsampled to low frames-per-second (FPS), and the most commonly used evaluation benchmarks are designed for low-FPS input as well. To address this shortcoming, we present FPS-Bench, a large video question-answering benchmark designed to evaluate VLMs' capabilities to understand video at high-frame rates. We introduce a new metric, the minimum frames-per-second (minFPS), which measures the minimum frame-rate required to solve a given question. While existing benchmarks require <1 minFPS, we rigorously curate more than 1000 questions from a diverse source of videos and manually verify minFPS for each example, leading to a benchmark that requires watching videos at on average 7 FPS to solve. Our evaluation of several state-of-the-art VLMs shows that they are severely lacking, achieving QA accuracy of 30% in the FPS-Bench multiple-choice task, while humans achieve 72% accuracy.

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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