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首次观测 · 记录 #1CVF · CVPR 2026:cvf:69fd0ff841fc3c12c80263293dbf1e02CVF · CVPR 2026

UniSH: Unifying Scene and Human Reconstruction in a Feed-Forward Pass

Li, Mengfei, Li, Peng, Zhang, Zheng, Lu, Jiahao, Zhao, Chengfeng, Xue, Wei, Liu, Qifeng, Peng, Sida 等 12 位作者

原文摘要

We present UniSH, a unified, feed-forward framework for joint metric-scale 3D scene and human reconstruction. A key challenge in this domain is the scarcity of large-scale, annotated real-world data, forcing a reliance on synthetic datasets. This reliance introduces a significant sim-to-real domain gap, leading to poor generalization, low-fidelity human geometry, and poor alignment on in-the-wild videos. To address this, we propose an innovative training paradigm that effectively leverages unlabeled in-the-wild data. Our framework bridges strong, disparate priors from scene reconstruction and HMR, and is trained with two core components: (1) a robust distillation strategy to refine human surface details by distilling high-frequency details from an expert depth model, and (2) a two-stage supervision scheme, which first learns coarse localization on synthetic data, then fine-tunes on real data by directly optimizing the geometric correspondence between the SMPL mesh and the human point cloud. This approach enables our feed-forward model to jointly recover high-fidelity scene geometry, human point clouds, camera parameters, and coherent, metric-scale SMPL bodies, all in a single forward pass. Extensive experiments demonstrate that our model achieves state-of-the-art performance on human-centric scene reconstruction and delivers highly competitive results on global human motion estimation, comparing favorably against both optimization-based frameworks and HMR-only methods. Project page: https://murphylmf.github.io/UniSH/.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6a145992dc338fa7edd18e483789a14dCVF · CVPR 2026

Beyond Strict Pairing: Arbitrarily Paired Training for High-Performance Infrared and Visible Image Fusion

Deng, Yanglin, Xu, Tianyang, Cheng, Chunyang, Li, Hui, Wu, Xiaojun, Kittler, Josef

原文摘要

Infrared and visible image fusion (IVIF) aims to synthesise complementary information from the two source modalities while preserving natural textures and salient thermal signatures simultaneously. Existing solutions predominantly rely on extensive sets of rigidly aligned image pairs for training. However, acquiring such data is often impractical due to the costly and labour-intensive alignment process. Besides, maintaining a rigid pairing setting during training restricts the volume of cross-modal relationships, thereby limiting the generalisation performance. To this end, this work challenges the necessity of Strictly Paired Training Paradigm (SPTP) by systematically investigating UnPaired and Arbitrarily Paired Training Paradigms (UPTP and APTP) for high-performance IVIF. We establish a theoretical objective of APTP, reflecting the complementary nature between UPTP and SPTP. More importantly, we develop a practical framework capable of significantly enriching cross-modal relationships even with severely limited and unaligned training data. To validate our propositions, three end-to-end lightweight baselines, alongside a set of innovative loss functions, are designed to cover three classic frameworks (CNN, Transformer, GAN). Comprehensive experiments demonstrate that the proposed APTP and UPTP are feasible and capable of training models on a severely limited and content-inconsistent infrared and visible dataset, achieving performance comparable to that of a dataset 100xlarger in SPTP. This finding fundamentally alleviates the cost and difficulty of data collection while enhancing model robustness from the data perspective, delivering a feasible solution for IVIF studies.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf: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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6a29b03d0418221811a9fb90177d0897CVF · CVPR 2026

Multi-Patch Global-to-Local Transformer Architecture For Efficient Flow Matching and Diffusion Model

Dao, Quan, Metaxas, Dimitris

原文摘要

Transformer architectures, particularly Diffusion Transformers (DiTs), have become widely used in diffusion and flow-matching models due to their strong performance compared to convolutional UNets. However, the isotropic design of DiTs processes the same number of patchified tokens in every block, leading to relatively heavy computation during training process. In this work, we introduce a multi-patch transformer design in which early blocks operate on larger patches to capture coarse global context, while later blocks use smaller patches to refine local details. This hierarchical design could reduces computational cost by up to 50% in GFLOPs while achieving good generative performance. In addition, we also propose improved designs for time and class embeddings that accelerate training convergence. Extensive experiments on the ImageNet dataset demonstrate the effectiveness of our architectural choices.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6a6422fb65b40ac3a1216fe3be1f0e3bCVF · CVPR 2026

AnchorSplat: Feed-Forward 3D Gaussian Splatting With 3D Geometric Priors

Zhang, Xiaoxue, Zheng, Xiaoxu, Yin, Yixuan, Zhao, Tiao, Tang, Kaihua, Mi, Michael Bi, Xu, Zhan, Chen, Dave Zhenyu

原文摘要

Recent feed-forward Gaussian reconstruction models adopt a pixel-aligned formulation that maps each 2D pixel to a 3D Gaussian, entangling Gaussian representations tightly with the input images. In this paper, we propose AnchorSplat, a novel feed-forward 3DGS framework for scene-level reconstruction that represents the scene directly in 3D space. AnchorSplat introduces an anchor-aligned Gaussian representation guided by 3D geometric priors (e.g., sparse point clouds, voxels, or RGB-D point clouds), enabling a more geometry-aware renderable 3D Gaussians that is independent of image resolution and number of views. This design substantially reduces the number of required Gaussians, improving computational efficiency while enhancing reconstruction fidelity. Beyond the anchor-aligned design, we utilize a Gaussian Refiner to adjust the intermediate Gaussians via merely a few forward passes. Experiments on the ScanNet++ v2 NVS benchmark demonstrate the SOTA performance, outperforming previous methods with more view-consistent and substantially fewer Gaussian primitives.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6a6c26de2a04bfb86266050200775aadCVF · CVPR 2026

Continual Distillation of Teachers from Different Domains

Michel, Nicolas, Wang, Maorong, He, Jiangpeng, Yamasaki, Toshihiko

原文摘要

Deep learning models continue to scale, with some requiring more storage than many large-scale datasets. Thus, we introduce a new paradigm: Continual Distillation (CD), where a student learns sequentially from a stream of teacher models without retaining access to earlier teachers. CD faces two challenges: teacher training data is unavailable, and teachers have varying expertise. We show that external unlabeled data enables Unseen Knowledge Transfer (UKT), allowing the student to acquire information from domains not present in the training data, while known to the teacher. We also show that sequential distillation causes Unseen Knowledge Forgetting (UKF) when transferred knowledge is lost after training on later teachers. To better trade off between UKT and UKF, we propose Self External Data Distillation (SE2D), a method that preserves logits on external data to stabilize learning across heterogeneous teachers. Experiments on multiple benchmarks show that SE2D reduces UKF and improves cross-domain generalization. The code and implementation for this work are publicly available at: https://github.com/Nicolas1203/continual_distillation

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6a8270b1124030ff41b9102cf1f1d917CVF · CVPR 2026

TWINGS: Thin Plate Splines Warp-aligned Initialization for Sparse-View Gaussian Splatting

Kim, Hyeseong, Son, Geonhui, Lee, Deukhee, Hwang, Dosik

原文摘要

Novel view synthesis from sparse-view inputs poses a significant challenge in 3D computer vision, particularly for achieving high-quality scene reconstructions with limited viewpoints. We introduce TWINGS, a framework that enhances 3D Gaussian Splatting (3DGS) by directly addressing point sparsity. We employ Thin Plate Splines (TPS), a smooth non-rigid deformation model that minimizes bending energy to estimate a globally coherent warp from control-point correspondences, to align backprojected points from estimated depth with triangulated 3D control points, yielding calibrated backprojected points. By sampling these calibrated points near the control points, TWINGS provides a fast and geometrically accurate initialization for 3DGS, ultimately improving structural detail preservation and color fidelity in reconstructed scenes. Extensive experiments on DTU, LLFF, and Mip-NeRF360 demonstrate that TWINGS consistently outperforms existing methods, delivering detailed and accurate reconstructions under sparse-view scenarios.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6abb7d835649aa02a3ffb6ac9a7bf38dCVF · CVPR 2026

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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6ac5d32a9a16c5c95c096ea1bd0a5bf5CVF · CVPR 2026

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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6ae3941279150d112c3984bcc7beda51CVF · CVPR 2026

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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6aecb47e137a532ed8e758d1b35972f9CVF · CVPR 2026

InverFill: One-Step Inversion for Enhanced Few-Step Diffusion Inpainting

Vu, Duc, Nguyen, Kien, Nguyen, Trong-Tung, Nguyen, Ngan, Nguyen, Phong, Nguyen, Khoi, Pham, Cuong, Tran, Anh

原文摘要

Recent diffusion-based models achieve photorealism in image inpainting but require many sampling steps, limiting practical use. Few-step text-to-image models offer faster generation, but naively applying them to inpainting yields poor harmonization and artifacts between the background and inpainted region. We trace this cause to random Gaussian noise initialization, which under low function evaluations causes semantic misalignment and reduced fidelity. To overcome this, we propose InverFill, a one-step inversion method tailored for inpainting that injects semantic information from the input masked image into the initial noise, enabling high-fidelity few-step inpainting. Instead of training inpainting models, InverFill leverages few-step text-to-image models in a blended sampling pipeline with semantically aligned noise as input, significantly improving vanilla blended sampling and even matching specialized inpainting models at low NFEs. Moreover, InverFill does not require real-image supervision and only adds minimal inference overhead. Extensive experiments show that InverFill consistently boosts baseline few-step models, improving image quality and text coherence without costly retraining or heavy iterative optimization.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6b3dbad989fa333c7496607338cbb564CVF · CVPR 2026

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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6b44ac8a775e827d48005b2dba6b5007CVF · CVPR 2026

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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6b6092c807dabe42411922d690368978CVF · CVPR 2026

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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6b6b518b8d3c958f24294576973e2975CVF · CVPR 2026

MDS-VQA: Model-Informed Data Selection for Video Quality Assessment

Zou, Jian, Xu, Xiaoyu, Wang, Zhihua, Wang, Yilin, Adsumilli, Balu, Ma, Kede

原文摘要

Learning-based video quality assessment (VQA) has advanced rapidly, yet progress is increasingly constrained by a disconnect between model design and dataset curation. Model-centric approaches often iterate on fixed benchmarks, while data-centric efforts collect new human labels without systematically targeting the weaknesses of existing VQA models. Here, we describe MDS-VQA, a model-informed data selection mechanism for curating unlabeled videos that are both difficult for the base VQA model and diverse in content. Difficulty is estimated by a failure predictor trained with a ranking objective, and diversity is measured using deep semantic video features, with a greedy procedure balancing the two under a constrained labeling budget. Experiments across multiple VQA datasets and models demonstrate that MDS-VQA identifies diverse, challenging samples that are particularly informative for active fine-tuning. With only a 5% selected subset per target domain, the fine-tuned model improves mean SRCC from 0.651 to 0.722 and achieves the top gMAD rank, indicating strong adaptation and generalization.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:6b896a50fd8b75ebe21607ca87410b20CVF · CVPR 2026

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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GDRO: Group-level Reward Post-training Suitable for Diffusion Models

Wang, Yiyang, Chen, Xi, Xu, Xiaogang, Liu, Yu, Zhao, Hengshuang

原文摘要

Recent advancements adopt online reinforcement learning (RL) from LLMs to text-to-image rectified flow diffusion models for reward alignment. The use of group-level rewards successfully aligns the model with the targeted reward. However, it faces challenges including low efficiency, dependency on stochastic samplers, and reward hacking. The problem is that rectified flow models are fundamentally different from LLMs: 1) For efficiency, online image sampling takes much more time and dominates the time of training. 2) For stochasticity, rectified flow is deterministic once the initial noise is fixed. Aiming at these problems and inspired by the effects of group-level rewards from LLMs, we design Group-level Direct Reward Optimization (GDRO). GDRO is a new post-training paradigm for group-level reward alignment that combines the characteristics of rectified flow models. Through rigorous theoretical analysis, we point out that GDRO supports full offline training that saves the large time cost for image rollout sampling. Also, it is diffusion-sampler-independent, which eliminates the need for the ODE-to-SDE approximation to obtain stochasticity. We also empirically study the reward hacking trap that may mislead the evaluation, and involve this factor in the evaluation using a corrected score that not only considers the original evaluation reward but also the trend of reward hacking. Extensive experiments demonstrate that GDRO effectively and efficiently improves the reward score of the diffusion model through group-wise offline optimization across the OCR and GenEval tasks, while demonstrating strong stability and robustness in mitigating reward hacking.

cs.CV

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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Basis-Oriented Low-rank Transfer for Few-Shot and Test-Time Adaptation

Park, Junghwan, Cho, Woojin, Heo, Junhyuk, Kwon, Darongsae, Lee, Kookjin

原文摘要

Adapting large pre-trained models to unseen tasks under tight data and compute budgets remains challenging. Meta-learning approaches explicitly learn good initializations, but they require an additional meta-training phase over many tasks, incur high training cost, and can be unstable. At the same time, the number of task-specific pre-trained models continues to grow, yet the question of how to transfer them to new tasks with minimal additional training remains relatively underexplored. We propose BOLT (Basis-Oriented Low-rank Transfer), a framework that reuses existing fine-tuned models not by merging weights, but instead by extracting an orthogonal, task-informed spectral basis and adapting within that subspace.In the offline phase, BOLT collects dominant singular directions from multiple task vectors and orthogonalizes them per layer to form reusable bases. In the online phase, we freeze these bases and train only a small set of diagonal coefficients per layer for the new task, yielding a rank-controlled update with very few trainable parameters. This design provides (i) a strong, training-free initialization for unseen tasks, obtained by pooling source-task coefficients--along with a lightweight rescaling step--while leveraging the shared orthogonal bases, and (ii) a parameter-efficient fine-tuning (PEFT) path that, in our experiments, achieves robust performance compared to common PEFT baselines as well as a representative meta-learned initialization. Our results show that constraining adaptation to a task-informed orthogonal subspace provides an effective alternative for unseen-task transfer.

cs.CV

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

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Toward Real-world Infrared Image Super-Resolution: A Unified Autoregressive Framework and Benchmark Dataset

Zou, Yang, Ma, Jun, Jiao, Zhidong, Li, Xingyuan, Jiang, Zhiying, Liu, Jinyuan

原文摘要

Infrared image super-resolution (IISR) under real-world conditions is a practically significant yet rarely addressed task. Pioneering works are often trained and evaluated on simulated datasets or neglect the intrinsic differences between infrared and visible imaging. In practice, however, real infrared images are affected by coupled optical and sensing degradations that jointly deteriorate both structural sharpness and thermal fidelity. To address these challenges, we propose Real-IISR, a unified autoregressive framework for real-world IISR that progressively reconstructs fine-grained thermal structures and clear backgrounds in a scale-by-scale manner via thermal-structural guided visual autoregression. Specifically, a Thermal-Structural Guidance module encodes thermal priors to mitigate the mismatch between thermal radiation and structural edges. Since non-uniform degradations typically induce quantization bias, Real-IISR adopts a Condition-Adaptive Codebook that dynamically modulates discrete representations based on degradation-aware thermal priors. Also, a Thermal Order Consistency Loss enforces a monotonic relation between temperature and pixel intensity, ensuring relative brightness order rather than absolute values to maintain physical consistency under spatial misalignment and thermal drift. We build FLIR-IISR, a real-world IISR dataset with paired LR-HR infrared images acquired via automated focus variation and motion-induced blur. Extensive experiments demonstrate the promising performance of Real-IISR, providing a unified foundation for real-world IISR and benchmarking. The dataset and code are available at: https://github.com/JZD151/Real-IISR.

cs.CV

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

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Defect Cue-Preserved Structural Feature Refinement for Few-Shot Anomaly Detection

Jiang, Le, Huang, Yan, Xu, Zhen, Xu, Yong, Wong, Hau-San, Wu, Si

原文摘要

Modern industrial quality control heavily relies on automated anomaly detection. While few-shot anomaly detection addresses the challenge of limited labeled data, real-world inspection faces a vast diversity of anomaly types, sizes, and shapes. We identify the primary cause for the anomaly detection difficulty as the progressive loss of detect cues as they pass through deep feature extraction pipelines. To counteract the defect cue fading, we propose a Defect Cue-Preserved Structural Feature Refinement model, referred to as DCP-SFR. Recognizing that early-stage cues are paramount, we design a conditional anomaly cue amplification module to produce an initial anomaly score map, which is then enhanced to increase the contrast between anomalous and normal regions. The amplified cues is subsequently used for reconstruction-based anomaly localization, by anchoring attention on true anomaly regions to preserve spatial integrity and prevent drift. Further, we incorporate a structure-aware segmentation refinement stage to improve anomaly segmentation in terms of edge alignment, thereby significantly improve boundary accuracy. On the MVTec AD and VisA benchmarks, DCP-SFR achieves state-of-the-art performance, with an image-level AUROC of 97.3% and a pixel-level AUROC of 98.2%, demonstrating strong cross-domain generalization performance.

cs.CV

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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Conversational Image Segmentation: Grounding Abstract Concepts with Scalable Supervision

Sahoo, Aadarsh, Gkioxari, Georgia

原文摘要

Conversational image segmentation grounds abstract, intent-driven concepts into pixel-accurate masks. Prior work on referring image grounding focuses on categorical and spatial queries (e.g., "left-most apple") and overlooks functional and physical reasoning (e.g., "where can I safely store the knife?"). We address this gap and introduce Conversational Image Segmentation (CIS) and ConvSeg, a benchmark spanning entities, spatial relations, intent, affordances, functions, safety, and physical reasoning. We also present ConvSeg-Net, which fuses strong segmentation priors with language understanding, and an AI-powered data engine that generates prompt-mask pairs without human supervision. We show that current language-guided segmentation models are inadequate for CIS, while ConvSeg-Net trained on our data engine achieves significant gains on ConvSeg and maintains strong performance on existing language-guided segmentation benchmarks. Project webpage: https://glab-caltech.github.io/converseg

cs.CV

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

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Hybrid Robust Collaborative Perception with LiDAR-4D Radar Fusion under Adverse Weather Conditions

Yang, Yuquan, Zhang, Hui, Lu, Wenyu, Zhang, Ziyin, Zhang, Chuanming, Xu, Xiaohua

原文摘要

Current collaborative perception systems have significantly improved 3D object detection performance. However, widely used LiDAR and camera systems often suffer performance degradation under adverse weather conditions. The weather-robust 4D radar provides a promising solution to address this challenge. Nevertheless, the effective fusion of sparse 4D radar measurements with degraded LiDAR data remains a significant challenge due to cross-modal corruption and information loss. In this work, we propose a novel hybrid robust collaborative perception framework (HRCP), designed to improve the collaborative perception performance under adverse weather conditions through LiDAR-4D radar fusion. Specifically, we introduce a hybrid collaboration strategy that considers their distinct physical properties and differently processes them during information transmission. Additionally, we propose a bidirectional cross-modal gating (BCMG) module that enables LiDAR and 4D radar to mutually validate feature reliability, ensuring consistent cross-modal representation, and an adaptive feature enhancement (AFE) module that enables comprehensive refinement of degraded and suppressed regions to mitigate information loss. Extensive experimental results demonstrate that our method outperforms previous state-of-the-art approaches under adverse weather conditions.

cs.CV

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

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StreamVLO: Streaming Visual-LiDAR Odometry with Cumulative Drift Compensation

Liu, Mengmeng, Liu, Jiuming, Yang, Michael Ying, Jiang, Chaokang, Li, Jiangtao, Zhang, Yunpeng, Wang, Hesheng, Nex, Francesco 等 9 位作者

原文摘要

We propose StreamVLO, a streaming visual-LiDAR odometry framework that performs unified spatio-temporal correlation with Mamba models and tackles the long-standing cumulative drift problem via an online Cumulative Drift Compensation scheme for localization in 4D dynamic environments. Specifically, StreamVLO introduces a unified spatio-temporal correlation module built on Mamba to fuse heterogeneous visual and LiDAR cues across multi-frame clips, overcoming the limited temporal exploration of prior pairwise methods. Furthermore, a Cumulative Drift Compensation module minimizes cumulative drift by iteratively learning residual corrections from multiple historical frames in a causal manner. To strengthen spatial feature representation on salient regions, we adopt a Keypoint-Aware Auxiliary Loss with a winner-takes-all strategy. StreamVLO achieves state-of-the-art performance on two commonly used autonomous driving datasets, reducing errors by 19% t_rel and 22% r_rel on KITTI, and by 18% ATE and 16% RPE on Argoverse, while remaining suitable for real-time deployment.

cs.CV

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

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Detect Any AI-Counterfeited Text Image

Qu, Chenfan, Zhong, Yiwu, Zhu, Xuekang, Li, Junchi, Jiang, Changjiang, liu, Jian, Jin, Lianwen

原文摘要

The rapid advancement of generative AI enables the creation of highly realistic text images, posing significant security risks from fraud and disinformation. However, research into robust detection is critically hampered by existing datasets that lack scale, diversity, and updated counterfeit techniques, as well as by models that fail to generalize. To address these deficiencies, we introduce DanceText, a large-scale, comprehensive dataset for AI-counterfeited text image detection. Constructed using our novel Creative Proposer pipeline, which automates the generation of diverse and realistic counterfeits, DanceText surpasses previous works by over 100-fold in multiple dimensions. It is the first to include counterfeits from multimodal large models, commercial software, and mobile apps, covering all major paradigms, including full-image generation, regional removal, and editing. Building on this dataset, we propose DS-Net, a novel and effective detection model. It features two key components: a Forensic Decoupling Encoder to extract generator-agnostic artifact features, and a Synergy Denoising Decoder that synergizes image-level classification with instance-level localization. Extensive experiments demonstrate that DS-Net achieves state-of-the-art performance, advancing the field toward robust and unified detection of AI-counterfeited text images in real-world scenarios. Both our code and dataset will be released publicly.

cs.CV

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

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DARC: Dual Adjustment Reasoning with Counterfactuals for Trustworthy Chest X-ray Classification

Liao, Zhifang, Li, Junhao, Ding, HaoKang, Song, Yucheng

原文摘要

Despite their impressive performance in multi-label classification of chest X-ray images (CXR), deep learning models are widely plagued by two types of spurious correlations: feature confounding arising from pathological co-occurrence and shortcut learning triggered by non-pathological visual confounders. These non-causal dependencies severely undermine the interpretability and robustness of models in real-world clinical settings. To address these challenges, we propose the Dual Adjustment Reasoning with Counterfactuals for Trustworthy Chest X-ray Classification (DARC) framework, the first to synergistically decouple both types of confounding sources from a causal mechanism perspective. At the data level, we construct CheXconf, the first pixel-level annotation dataset of non-pathological visual confounders in CXR, comprising 40,213 annotated instances across 11 categories. This provides a solid foundation for accurately modeling these confounders. At the methodological level, we design a novel dual-stream causal learning architecture. Its Global Stream leverages the back-door adjustment criterion with CheXconf to explicitly block spurious paths from non-pathological confounders. Concurrently, the Local Stream employs counterfactual reasoning, constrained by anatomical priors, to disentangle the visual coupling of co-occurring pathologies. Experiments on large-scale public benchmarks demonstrate that our method achieves significant improvements in task performance, interpretability, and robustness.

cs.CV

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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