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首次观测 · 记录 #1CVF · CVPR 2026:cvf:4790a2ec6028974e0f531de2d19a0642CVF · CVPR 2026

FEAT: Fashion Editing and Try-On from Any Design

Kwon, Soye, Lee, Keonyoung, Jung, Dahuin, Lee, Jaekoo

原文摘要

Fashion design aims to express a designer's creative intent and to depict how garments interact with the human body. Recent methods condition on multimodal inputs to support garment editing and virtual try-on. However, existing methods still (i) confine design to garment-related images, excluding creative design sources such as artwork, abstract imagery, and natural photographs, and (ii) cannot support complete outfits, including accessories. We present FEAT (Fashion Editing And Try-On from Any Design), a method that enables editing and try-on across garments and accessories using diverse design sources. To achieve this, we introduce Disentangled Dual Injection (DDI). It takes both apparel and non-apparel design sources and selectively injects design cues via content and style disentanglement. Furthermore, we propose Orthogonal-Guided Noise Fusion (OGNF), a training-free mechanism that removes residual garments via orthogonal projection and applies region-specific noise strategies to enable virtual try-on for both garments and accessories. Extensive experiments demonstrate that FEAT achieves state-of-the-art performance in design flexibility, prompt consistency, and visual realism.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:4792464f8428ff8cfb3d9999126aa3b3CVF · CVPR 2026

Image-based Outlier Synthesis With Training Data

Regmi, Sudarshan

原文摘要

Out-of-distribution (OOD) detection is critical to ensure the safe deployment of deep learning models in critical applications. Deep learning models can often misidentify OOD samples as in-distribution (ID) samples. This vulnerability worsens in the presence of spurious correlation in the training set. Likewise, in fine-grained classification settings, detection of fine-grained OOD samples becomes inherently challenging due to their high similarity to ID samples. However, current research on OOD detection has focused instead largely on relatively easier (conventional) cases. Even the few recent works addressing these challenging cases rely on carefully curated or synthesized outliers, ultimately requiring external data. This motivates our central research question: "Can we innovate OOD detection training framework for fine-grained and spurious settings without requiring any external data at all?" In this work, we present a unified Approach to Spurious, fine-grained, and Conventional OOD Detection (\ASCOOD) that eliminates the reliance on external data. First, we synthesize virtual outliers from ID data by approximating the destruction of invariant features. Specifically, we propose to add gradient attribution values to ID inputs to disrupt invariant features while amplifying true-class logit, thereby synthesizing challenging near-manifold virtual outliers. Then, we simultaneously incentivize ID classification and predictive uncertainty towards virtual outliers. For this, we further propose to leverage standardized features with z-score normalization. ASCOOD effectively mitigates impact of spurious correlations and encourages capturing fine-grained attributes. Extensive experiments across 7 datasets and comparisons with 30+ methods demonstrate merit of ASCOOD in spurious, fine-grained and conventional settings.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:47a1c05f86a52b45b550a7b997709440CVF · CVPR 2026

TaskIT: Memory-Efficient Fine-Tuning of Multi-LoRA LLMs via Cross-Task Importance Transfer

Fang, Cheng, Zhou, Zimu, Ma, Ke, Guo, Bin

原文摘要

On-device AI systems increasingly adopt a single foundation model equipped with task-specific Low-Rank Adaptation (LoRA) modules, forming a multi-LoRA LLM that supports multiple tasks.We study how to adapt such a model to a new task on memory-constrainted devices.Although LoRA reduces trainable parameters, fine-tuning a full set of modules remains memory-intensive.To improve efficiency, we apply sparse updating, training a subset of LoRA modules within the memory budget.However, existing sparse updating methods assume all candidate parameters are instantiated and cannot estimate the importance of modules that do not yet exist, while prior memory models designed for sequential networks fail to capture the blockwise parallel structure of Transformers.We propose TaskIT, a framework for memory-efficient fine-tuning via cross-task importance transfer. TaskIT predicts pre-insertion module importance by transferring from previously tuned tasks and employs a block-based memory predictor that captures parallel and sequential dependencies of Transformer blocks. A dynamic programming scheduler then selects module locations, numbers, and ranks to maximize accuracy within the memory budget.Experiments on uni-modal and cross-modal benchmarks show that TaskIT achieves superior accuracy-memory tradeoffs compared with Zero-FT, non-LoRA, and LoRA-based fine-tuning methods.

cs.CV · cs.CL

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:47ad2a47cc0da82e7b03b5d883973e05CVF · CVPR 2026

Semantic Scale Space: A Framework for Controllable Image Abstraction

Mishiba, Kazu

原文摘要

Image abstraction, a fundamental component of non-photorealistic rendering (NPR), aims to simplify photographs into stylized depictions while preserving perceptually important structures. A central difficulty is selectivity: removing fine textures while preserving semantically meaningful boundaries. Existing approaches often expose only a few entangled controls, so the amount of smoothing and the set of preserved structures cannot be adjusted in a clear and intuitive manner. We propose the Semantic Scale Space (SSS), a two-dimensional abstraction framework parameterized by smoothing strength and semantic granularity. SSS externalizes the stopping set by using a controllable semantic boundary detector to specify which structures act as barriers to smoothing, independently of how far simplification proceeds within homogeneous regions. We further introduce Adaptive Granularity Scheduling Smoothing (AGSS), a concrete traversal policy in this space that combines a donor-gated diffusion operator with a fine-to-coarse granularity schedule. To evaluate selectivity fairly, we also introduce an effect-matched protocol based on a Region Homogeneity Index, which compares methods under matched smoothing levels. On the SBD benchmark, AGSS achieves higher boundary preservation and lower geometric drift than strong baselines at the same degree of smoothing. On DIV2K, a user study further shows that downstream NPR results built on AGSS-based abstractions are consistently preferred. These results demonstrate that SSS and AGSS provide a practical framework and method for controllable image abstraction in modern creative applications.

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:47b75a061a15823d851d4e777a4cea4fCVF · CVPR 2026

D2Dewarp: Dual Dimensions Geometric Representation Learning Based Document Image Dewarping

Li, Heng, Wu, Xiangping, Chen, Qingcai

原文摘要

Document image dewarping remains a challenging task in the deep learning era. While existing methods have improved by leveraging text line awareness, they typically focus only on a single horizontal dimension. In this paper, we propose a fine-grained deformation perception model that focuses on Dual Dimensions of document horizontal-vertical-lines to improve document Dewarping called D2Dewarp. It can perceive distortion trends in different directions across document details. To combine the horizontal and vertical granularity features, an effective fusion module based on X and Y coordinate is designed to facilitate interaction and constraint between the two dimensions for feature complementarity. Due to the lack of annotated line features in current public dewarping datasets, we also propose an automatic fine-grained annotation method using public document texture images and automatic rendering engine to build a new large-scale distortion training dataset named DocDewarpHV. The code and dataset will be publicly released. On three public Chinese and English benchmarks, both quantitative and qualitative results show that our method achieves better rectification results compared with the state-of-the-art methods. The code and dataset are available at https://github.com/xiaomore/D2Dewarp.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:47bc8f506e554e29f34e6685abc1de16CVF · CVPR 2026

Bulk RNA-seq Guided Multi-modal Detection of Anomalous Regions in Human Cancer via Spatial Transcriptomics

Shi, Hang, Yang, Ruocheng, You, Wenjie, Huang, Zhilin, Zhang, Daoqiang, Shao, Wei

原文摘要

Spatial transcriptomics (ST) has emerged as a revolutionary approach in the field of tissue analysis that can offer spatial resolved molecular insights for the identification of anomalous regions (AR) on human cancers. Current ST-based methods for detecting AR focus narrowly on the molecular features of local tissue spots, overlooking the matched bulk RNA-seq data that contains crucial diagnostic information. This oversight limits their effectiveness in identifying subtle or heterogeneous tumors, where accurate detection depends on broader genetic context. Besides the genomic signatures, the pathological images can also provide rich visual information to reflect the morphology of AR. To utilize the patient-level diagnostic knowledge and harness complementary information from both histology images and ST, we develop a Bulk RNA-seq Guided Multi-modal Anomalous Regions Detection method (BRGMAR) for the identification of AR from human tissues. Specifically, to effectively model the dependencies in ST, we introduce a Dynamic Multi-Relational Graph Learning (DMRGL) module to adaptively capture complex relationships in ST, including both spatial proximity and gene expression similarity. Then, we design an Optimal Transportation-based Gene Module Alignment (OTGMA) approach to align ST data with patient-level bulk RNA-seq data by matching the compositional and functional similarities of their corresponding gene modules. Finally, we combine the learned genomic features with pathological image representations for accurate AR detection. We evaluate our method on three public available ST datasets for the purpose of identifying cancerous regions from normal tissues, and the experimental results demonstrate the advantage of our method in comparison with the existing studies.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:47c3f618e9106fea75ebb67852a70927CVF · CVPR 2026

TAS-LoRA: Transformer Architecture Search with Mixture-of-LoRA Experts

Jeon, Jeimin, Lee, Hyunju, Ham, Bumsub

原文摘要

Transformer architecture search (TAS) discovers optimal vision transformer (ViT) architectures automatically, reducing human effort to manually design ViTs. However, existing TAS methods suffer from the feature collapse problem, where subnets within a supernet fail to learn subnet-specific features, mainly due to the shared weights in a supernet, limiting the performance of individual subnets. To address this, we propose TAS-LoRA, a novel method that introduces parameter-efficient low-rank adaptation (LoRA) to enable subnet-specific feature learning, while maintaining computational efficiency. TAS-LoRA incorporates a Mixture-of-LoRA-Experts (MoLE) strategy, where a lightweight router dynamically assigns LoRA experts based on subnet architectures, and introduces a group-wise router initialization technique to encourage diverse feature learning across experts early in training. Extensive experiments on ImageNet and several transfer learning benchmarks, including CIFAR-10/100, Flowers, CARS, and INAT-19, demonstrate that TAS-LoRA mitigates feature collapse effectively, improving performance over state-of-the-art TAS methods significantly.

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:47cb7d1a7b7fdf6f1718e9abcf0a5ce0CVF · CVPR 2026

Explaining Object Detectors via Collective Contribution of Pixels

Yamauchi, Toshinori, Kera, Hiroshi, Kawamoto, Kazuhiko

原文摘要

Visual explanations for object detectors are crucial for enhancing their reliability. Object detectors identify and localize instances by assessing multiple visual features collectively. When generating explanations, overlooking these collective influences in detections may lead to missing compositional cues or capturing spurious correlations. However, existing methods typically focus solely on individual pixel contributions, neglecting the collective contribution of multiple pixels. To address this limitation, we propose a game-theoretic method based on Shapley values and interactions to explicitly capture both individual and collective pixel contributions. Our method provides explanations for both bounding box localization and class determination, highlighting regions crucial for detection. Extensive experiments demonstrate that the proposed method identifies important regions more accurately than state-of-the-art methods. The code is available at https://github.com/tttt-0814/VX-CODE.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:47e6cb7411f8e674ad207d17bab1bf1cCVF · CVPR 2026

InvCoSS: Inversion-driven Continual Self-supervised Learning in Medical Multi-modal Image Pre-training

Luo, Zihao, Rui, Shaohao, Tang, Zhenyu, Wang, Guotai, Wang, Xiaosong

原文摘要

Continual self-supervised learning (CSSL) in medical imaging trains a foundation model sequentially, alleviating the need for collecting multi-modal images for joint training and offering promising improvements in downstream performance while preserving data privacy. However, most existing methods still rely on replaying data from previous stages to prevent catastrophic forgetting, which compromises privacy and limits their applicability in real-world scenarios where data transfer across sites is often restricted. In this work, we propose InvCoSS, an inversion-driven continual self-supervised learning framework for medical multi-modal image pre-training. Specifically, after training on a previous task, InvCoSS inverts the pre-trained self-supervised model to generate synthetic images that approximate the original training distribution. These synthetic images are then combined with data from the new task for joint optimization, which effectively mitigates catastrophic forgetting while strictly adhering to the constraint of no access to previous real data. Furthermore, to improve the fidelity of synthetic images, we introduce a novel InvUNet with a multi-scale fusion architecture to restore both high- and low-frequency components of the inverted images. To enhance diversity and prevent mode collapse, we design a repulsive representation-learning mechanism that encourages a diverse feature space for synthetic images without class guidance. Extensive experiments across nine downstream tasks validate the effectiveness of InvCoSS, achieving performance comparable to or even superior to prior data-replay methods while significantly reducing storage requirements and eliminating data privacy constraints.

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:483081f89911dc16ecea93b65f3f7ac5CVF · CVPR 2026

R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII

Zhou, Zewei, Zou, Jiajun, Zhang, Jiajia, Yang, Ao, He, Ruichao, Zhou, Haozheng, Liu, Ao, Liu, Jiawei 等 11 位作者

原文摘要

Graph neural networks (GNNs) are increasingly applied to physical design tasks such as congestion prediction and wirelength estimation, yet progress is hindered by inconsistent circuit representations and the absence of controlled evaluation protocols. We present R2G (RTL-to-GDSII), a multi-view circuit-graph benchmark suite that standardizes five stage-aware views with information parity (every view encodes the same attribute set, differing only in where features attach) over 30 open-source IP cores (up to 106 nodes/edges). R2G provides an end-to-end DEF-to-graph pipeline spanning synthesis, placement, and routing stages, together with loaders, unified splits, domain metrics, and reproducible baselines. By decoupling representation choice from model choice, R2G isolates a confound that prior EDA and graph-ML benchmarks leave uncontrolled. In systematic studies with GINE, GAT, and ResGatedGCN, we find: (i) view choice dominates model choice, with Test R2 varying by more than 0.3 across representations for a fixed GNN; (ii) node-centric views generalize best across both placement and routing; and (iii) decoder-head depth (3-4 layers) is the primary accuracy driver, turning divergent training into near-perfect predictions (R2 > 0.99). Code and datasets: https://github.com/ShenShan123/R2G.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:483577e203d47e05b6b944a584274381CVF · CVPR 2026

Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models

Xu, Yexing, Feng, Wei, Zhang, Shen, Wang, Haohan, Qin, Yuxin, Li, Yaoyu, Ma, Ao, Luo, Yuhao 等 18 位作者

原文摘要

Generating realistic and user-preferred advertisements is a key challenge in e-commerce. Existing approaches utilize multiple independent models driven by click-through-rate (CTR) to controllably create attractive image or text advertisements. However, their pipelines lack cross-modal perception and rely on CTR that only reflects average preferences. Therefore, we explore jointly generating personalized image-text advertisements from historical click behaviors. We first design a Unified Advertisement Generative model (Uni-AdGen) that employs a single autoregressive framework to produce both advertising images and texts. By incorporating a foreground perception module and instruction tuning, Uni-AdGen enhances the realism of the generated content. To further personalize advertisements, we equip Uni-AdGen with a coarse-to-fine preference understanding module that effectively captures user interests from noisy multimodal historical behaviors to drive personalized generation. Additionally, we construct the first large-scale Personalized Advertising image-text dataset (PAd1M) and introduce a Product Background Similarity (PBS) metric to facilitate training and evaluation. Extensive experiments show that our method outperforms baselines in general and personalized advertisement generation. The dataset and codes will be released after acceptance.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:483a2daf5851d31448a4b02eb628b5a7CVF · CVPR 2026

Graph-to-Frame RAG: Visual-Space Knowledge Fusion for Training-Free and Auditable Video Reasoning

Yang, Songyuan, Yu, Weijiang, Liu, Ziyu, Tang, Guijian, Yang, Wenjing, Tan, Huibin, Xiao, Nong

原文摘要

When video reasoning requires external knowledge, many systems with large multimodal models (LMMs) adopt retrieval augmentation to supply the missing context. Appending textual or multi-clip evidence, however, forces heterogeneous signals into a single attention space. We observe diluted attention and higher cognitive load even on non-long videos. The bottleneck is not only what to retrieve but how to represent and fuse external knowledge with the video backbone.We present **Graph-to-Frame RAG (G2F-RAG)**, a training free and auditable paradigm that delivers knowledge in the visual space. On the offline stage, an agent builds a problem-agnostic video knowledge graph that integrates entities, events, spatial relations, and linked world knowledge. On the online stage, a hierarchical multi-agent controller decides whether external knowledge is needed, retrieves a minimal sufficient subgraph, and renders it as a single reasoning frame appended to the video. LMMs then perform joint reasoning in a unified visual domain. This design reduces cognitive load and leaves an explicit, inspectable evidence trail.G2F-RAG is plug-and-play across backbones and scales. It yields consistent gains on diverse public benchmarks, with larger improvements in knowledge-intensive settings. Ablations further confirm that knowledge representation and delivery matter. G2F-RAG reframes retrieval as visual space knowledge fusion for robust and interpretable video reasoning.

cs.CV · cs.MA

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:4841fda3c89ea807b2b3855705a6a7aaCVF · CVPR 2026

Cinematic Audio Source Separation Using Visual Cues

Zhang, Kang, Lee, Suyeon, Senocak, Arda, Chung, Joon Son

原文摘要

Cinematic Audio Source Separation (CASS) aims to decompose mixed film audio into speech, music, and sound effects, enabling applications like dubbing and remastering. Existing CASS approaches are audio-only, overlooking the inherent audio-visual nature of films, where sounds often align with visual cues. We present the first framework for audio-visual CASS (AV-CASS), leveraging visual context to enhance separation quality. Our method formulates CASS as a conditional generative modeling problem using conditional flow matching, enabling multimodal audio source separation. To address the lack of cinematic datasets with isolated sound tracks, we introduce a training data synthesis pipeline that pairs in-the-wild audio and video streams (e.g., facial videos for speech, scene videos for effects) and design a dedicated visual encoder for this dual-stream setup. Trained entirely on synthetic data, our model generalizes effectively to real-world cinematic content and achieves strong performance on synthetic, real-world, and audio-only CASS benchmarks. Code and demo are available at https://cass-flowmatching.github.io.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:4853fd5cca462dce55f07b35edfb5efeCVF · CVPR 2026

Seeing Motion Through Polarity for Event-based Action Recognition

Cao, Meiqi, Zhang, Jiachao, Jiang, Xin, Yan, Rui, Yao, Yazhou, Li, Zechao, Shu, Xiangbo

原文摘要

Event-based Action Recognition (EAR) provides a promising pathway for understanding dynamic behaviors under challenging conditions. Recent progress in vision-language models has introduced a cross-modal learning paradigm into EAR, enabling models to associate event streams with textual semantics for enhancing conceptual understanding. However, existing methods typically overlook the intrinsic polarity-driven motion cues that are fundamental to event data, leading to suboptimal spatiotemporal representations. To address this limitation, we propose a POlarity Knowledge Enhanced framework (POKER), which explicitly incorporates event polarity-aware motion knowledge across visual and textual modalities.POKER consists of two synergistic components: Polarity Motion Capturer (PMC) and Polarity Motion Reasoner (PMR). Specifically, PMC decouples positive and negative polarities to capture polarity-sensitive motion cues, while PMR semantically analyzes polarity-induced motion dynamics via large language models. Through the polarity alignment, POKER couples semantic reasoning with visual dynamics, achieving more discriminative representations. Extensive experiments on multiple benchmarks demonstrate that POKER enhances performance across diverse event representations.

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:4859a36111eb75407d97ec0715bfe868CVF · CVPR 2026

Plug-and-Play Incomplete Multi-View Clustering via Janus-Faced Affinity Learning with Topology Harmonization

Yu, Shengju, Liu, Suyuan, Shao, Wenhao, Wang, Siwei, Liang, Ke, Yang, Xihong, Li, Tiejun, Liu, Xinwang

原文摘要

Prevailing incomplete multi-view clustering (IMVC) approaches typically fail to account for the interference of view-exclusive artifacts when learning view-consensus representations, which could compromise the fidelity of the resulting similarity measure. Moreover, inconsistencies in anchor order across views may distort the graph structure, impairing the clustering performance. The reliance on carefully-tuned regularization hyper-parameters also usually undermines the model's practical utility. To alleviate these issues, we propose a plug-and-play IMVC framework named PJFTH that incorporates Janus-faced affinity learning with topology harmonization. It explicitly models the exclusive-to-consensus interplay, derives a view-private graph from each view, and adaptively integrates them into a global consensus affinity according to the respective view's intrinsic characteristics. Furthermore, a permutation transformation with unary encoding constraints is applied to anchor matrix, realigning anchor topology while preserving the values. This process synchronizes anchor order prior to similarity integration and maintains original anchor properties. Notably, all components are coupled seamlessly and optimized in a joint manner. Also, the provable overall linear complexity further enlarges its scalability and practicality. Experimental results confirm that PJFTH receives competitive performance compared to several leading methods.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:486f0dcd9e84c1583508db7d222a0a96CVF · CVPR 2026

Beyond Text: Visual Description Assembly by Probabilistic Model for CLIP-based Weakly Supervised Semantic Segmentation

Qiu, Xianglin, Wang, Jian, Wang, Xiaolei, Zhang, Zhen, Xiao, Jimin

原文摘要

Contrastive Language-Image Pre-training (CLIP) offers a new paradigm for Weakly Supervised Semantic Segmentation (WSSS) by generating Class Activation Maps (CAMs) from text-image alignment. Existing methods primarily rely on hand-crafted templates or general attribute descriptions generated by a large language model to construct text prototypes for querying visual features. However, these strategies faces two major limitations: the inherent modality gap in CLIP prevents text prototypes achieving tight alignment with visual features; and their static text prototypes cannot adaptively respond to target instances that exhibit diverse visual attributes. To address these challenges, our key insight is to directly construct instance-specific visual description prototype as query, thereby bypassing the suboptimal static text description optimization. To this end, we propose the Visual Description Assembly (VDA) framework. It employs a probabilistic model to map complex CLIP visual features into a structured latent space. This latent space allows us to explicitly disentangle and aggregate varied visual attributes, and then dynamically assemble them into instance-specific visual prototypes. Furthermore, to enhance the robustness of this prototype, we adaptively incorporate the semantically stable text prototype into it as the final query for generating superior CAMs. Experimental results show our method outperforms existing baselines, achieving state-of-the-art performance on WSSS benchmarks.

cs.CV · cs.CL

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:487464b7bd108eb2b72a8d068e7c1448CVF · CVPR 2026

HyperGait: Unleashing the Power of Parsing for Gait Recognition in the Wild via Hypergraph

Zheng, Jinkai, Wei, Jiaqing, Jin, Xinxiang, Sun, Yaoqi, Sheng, Xichun, Li, Ming, Qu, Liangqiong, Liu, Xinchen 等 9 位作者

原文摘要

In recent years, the gait parsing sequence has become increasingly popular due to its higher information entropy than the binary silhouette and the keypoint-based skeleton. However, existing parsing-based gait recognition methods have not fully explored the complex, non-linear relationships between features at different positions, semantic, and temporal dynamics levels, i.e., higher-order correlations. To unleash the power of parsing between human body parts and temporal dynamics, this paper proposes a novel hypergraph-based gait recognition framework, named HyperGait. The HyperGait contains a global head and two elaborately-designed modules. In particular, the Spatial Hypergraph Convolutional Module (SHCM) and the Temporal Hypergraph Convolutional Module (THCM) are designed to explore the high-order spatial-level and temporal-level features, respectively.The SHCM extracts fine-grained relationships between human body parts through the hypergraph.The THCM performs the high-order temporal information between temporally related human body parts.Comprehensive experiments on two large-scale gait datasets, i.e., Gait3D and SUSTech1K, show the superior performance of our proposed HyperGait.In highly challenging real-world scenarios, with only parsing as input, our HyperGait achieves the Rank-1 accuracy of 80.5% on the Gait3D dataset.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:4874c46a8ba1655ecebf7699d6926cabCVF · CVPR 2026

Disentangled Textual Priors for Diffusion-based Image Super-Resolution

Jiang, Lei, Liu, Xin, Tong, Xinze, Li, Zhiliang, Liu, Jie, Tang, Jie, Wu, Gangshan

原文摘要

Image Super-Resolution (SR) aims to reconstruct high-resolution images from degraded low-resolution inputs. While diffusion-based SR methods offer powerful generative capabilities, their performance heavily depends on how semantic priors are structured and integrated into the generation process. Existing approaches often rely on entangled or coarse-grained priors that mix global layout with local details, or conflate structural and textural cues, thereby limiting semantic controllability and interpretability. In this work, we propose DTPSR, a novel diffusion-based SR framework that introduces disentangled textual priors along two complementary dimensions: spatial hierarchy (global vs. local) and frequency semantics (low- vs. high-frequency). By explicitly separating these priors, DTPSR enables the model to simultaneously capture scene-level structure and object-specific details with frequency-aware semantic guidance. The corresponding embeddings are injected via specialized cross-attention modules, forming a progressive generation pipeline that reflects the semantic granularity of visual content--from global layout to fine-grained textures. To support this paradigm, we construct DisText-SR, a large-scale dataset containing approximately 95,000 image-text pairs with carefully disentangled global, low-frequency, and high-frequency descriptions. To further enhance controllability and consistency, we adopt a multi-branch classifier-free guidance strategy with frequency-aware negative prompts to suppress hallucinations and semantic drift. Extensive experiments on synthetic and real-world benchmarks show that DTPSR achieves high perceptual quality, competitive fidelity, and strong generalization across diverse degradation scenarios.

cs.CV

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

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SharpTimeGS: Sharp and Stable Dynamic Gaussian Splatting via Lifespan Modulation

Liao, Zhanfeng, Zhang, Jiajun, Tu, Hanzhang, Wang, Zhixi, Gao, Yunqi, Zhang, Hongwen, Liu, Yebin

原文摘要

Novel view synthesis of dynamic scenes is fundamental to achieving photorealistic 4D reconstruction and immersive visual experiences. Recent progress in Gaussian-based representations has significantly improved real-time rendering quality, yet existing methods still struggle to maintain a balance between long-term static and short-term dynamic regions in both representation and optimization. To address this, we present SharpTimeGS, a lifespan-aware 4D Gaussian framework that achieves temporally adaptive modeling of both static and dynamic regions under a unified representation. Specifically, we introduce a learnable lifespan parameter that reformulates temporal visibility from a Gaussian-shaped decay into a flat-top profile, allowing primitives to remain consistently active over their intended duration and avoiding redundant densification. In addition, the learned lifespan modulates each primitive's motion, reducing drift in long-lived static points while retaining unrestricted motion for short-lived dynamic ones. This effectively decouples motion magnitude from temporal duration, improving long-term stability without compromising dynamic fidelity. Moreover, we design a lifespan-velocity-aware densification strategy that mitigates optimization imbalance between static and dynamic regions by allocating more capacity to regions with pronounced motion while keeping static areas compact and stable. Extensive experiments on multiple benchmarks demonstrate that our method achieves state-of-the-art performance while supporting real-time rendering up to 4K resolution at 100 FPS on one RTX 4090.

cs.CV

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

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Progressive Multi-cue Alignment for Unaligned RGBT Tracking

Jin, Jiandong, Li, Chenglong, Feng, Hao, Lu, Andong, Huang, Lili, Tang, Jin

原文摘要

Unaligned RGBT tracking aims to achieve robust target localization across spatially misaligned RGB and thermal infrared (TIR) videos, a crucial challenge for deploying RGBT tracking in real-world scenarios. Existing methods often calculate all cross-modal alignment parameters (i.e., spatial shift and scale change) simultaneously, but suffer from two major limitations. 1) They are difficult to adapt to different degrees of unaligned difficulty during tracking. 2) They usually require complex models to handle challenging scenarios, resulting in a large computational burden. To overcome these limitations, we propose a novel Progressive Multi-cue Alignment framework called PMATrack, which disentangles the calculation of cross-modal alignment parameters in a progressive manner and dynamically selects appropriate cues to handle different challenges, thereby enabling robust and efficient unaligned RGBT tracking. In particular, PMATrack divides the cross-modal alignment parameter estimation into three stages to progressively perform center offset computation, scale transformation estimation, and global refinement. At each stage, we design a difficulty-aware router to adaptively select the appropriate alignment expert based on the cross-modal alignment complexity, thereby reducing computational redundancy. In addition, we build a high-quality video benchmark called MUART244 to facilitate the comprehensive evaluation of different unaligned RGBT tracking algorithms. Extensive experiments demonstrate the outstanding performance of PMATrack against existing state-of-the-art methods. The code and dataset will be available at https://github.com/NOP1224/Unaligned_RGBT_Tracking.

cs.CV

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

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LRDUN: A Low-Rank Deep Unfolding Network for Efficient Spectral Compressive Imaging

Huang, He, Guo, Yujun, He, Wei

原文摘要

Deep unfolding networks (DUNs) have achieved remarkable success and become the mainstream paradigm for spectral compressive imaging (SCI) reconstruction. Existing DUNs are derived from full-HSI imaging models, where each stage operates directly on the high-dimensional HSI, refining the entire data cube based on the single 2D coded measurement. However, this paradigm leads to computational redundancy and suffers from the ill-posed nature of mapping 2D residuals back to 3D space of HSI. In this paper, we propose two novel imaging models corresponding to the spectral basis and subspace image by explicitly integrating low-rank (LR) decomposition with the sensing model. Compared to recovering the full HSI, estimating these compact low-dimensional components significantly mitigates the ill-posedness. Building upon these novel models, we develop the Low-Rank Deep Unfolding Network (LRDUN), which jointly solves the two subproblems within an unfolded proximal gradient descent (PGD) framework. Furthermore, we introduce a Generalized Feature Unfolding Mechanism (GFUM) that decouples the physical rank in the data-fidelity term from the feature dimensionality in the prior module, enhancing the representational capacity and flexibility of the network. Extensive experiments on simulated and real datasets demonstrate that the proposed LRDUN achieves state-of-the-art (SOTA) reconstruction quality with significantly reduced computational cost. Code is available at https://github.com/huang-he99/LRDUN.

cs.CV

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

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Test-Time 3D Occupancy Prediction

Zhang, Fengyi, Sun, Xiangyu, Yang, Huitong, Zhang, Zheng, Huang, Zi, Luo, Yadan

原文摘要

Self-supervised 3D occupancy prediction offers a promising solution for understanding complex driving scenes without requiring costly 3D annotations. However, training dense occupancy decoders to capture fine-grained geometry and semantics can demand hundreds of GPU hours, and once trained, such models struggle to adapt to varying voxel resolutions or novel object categories without extensive retraining. To overcome these limitations, we propose a practical and flexible test-time occupancy prediction framework termed TT-Occ. Our method incrementally constructs, optimizes, and voxelizes time-aware 3D Gaussians from raw sensor streams by integrating vision foundation models (VFMs) at runtime. The flexible representation of 3D Gaussians enables voxelization at arbitrary user-specified resolutions, while the strong generalization capability of VFMs supports accurate perception and open-vocabulary recognition without requiring any network training or fine-tuning. To validate the generality and effectiveness of our framework, we present two variants: a LiDAR-based version and a vision-centric version, and conduct extensive experiments on the Occ3D-nuScenes and nuCraft benchmarks under varying voxel resolutions. Experimental results show that TT-Occ significantly outperforms existing computationally expensive pretrained self-supervised counterparts. Code is available at https://github.com/Xian-Bei/TT-Occ.

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

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CaliTex: Geometry-Calibrated Attention for View-Coherent 3D Texture Generation

Liu, Chenyu, Chen, Hongze, Bao, Jingzhi, Zhu, Lingting, Zhang, Runze, Chen, Weikai, Hu, Zeyu, Yin, Yingda 等 10 位作者

原文摘要

Despite major advances brought by diffusion-based models, current 3D texture generation systems remain hindered by cross-view inconsistency -- textures that appear convincing from one viewpoint often fail to align across others. We find that this issue arises from attention ambiguity, where unstructured full attention is applied indiscriminately across tokens and modalities, causing geometric confusion and unstable appearance-structure coupling.To address this, we introduce CaliTex, a framework of geometry-calibrated attention that explicitly aligns attention with 3D structure.It introduces two modules: Part-Aligned Attention that enforces spatial alignment across semantically matched parts, and Condition-Routed Attention which routes appearance information through geometry-conditioned pathways to maintain spatial fidelity.Coupled with a two-stage diffusion transformer, CaliTex makes geometric coherence an inherent behavior of the network rather than a byproduct of optimization.Empirically, CaliTex produces seamless and view-consistent textures and outperforms both open-source and commercial baselines.

cs.CV

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

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Mechanisms of Object Localization in Vision-Language Models

Schaumlöffel, Timothy, Vilas, Martina G., Roig, Gemma

原文摘要

Visually-grounded language models (VLMs) are highly effective in linking visual and textual information, yet they often struggle with basic classification and localization tasks. While classification mechanisms have been studied more extensively, the processes that support object localization remain poorly understood. In this work, we investigate two representative families, LLaVA-1.5 and InternVL-3.5, using a suite of mechanistic interpretability tools, including token ablations, attention knockout, and causal mediation analysis. We find that localization is driven by a containerization mechanism in which object-aligned tokens define the spatial extent of the object, while the semantic arrangement of tokens within those boundaries is largely irrelevant to the predicted box. Only a very small set of attention heads mediates the causal effect for both classification and localization, concentrating in early-mid layers for LLaVA and mid-late layers for InternVL. The two tasks share some early processing but ultimately depend on largely distinct specialized heads. Overall, we provide the first layer- and head-level account of localization in VLMs, revealing narrow computational pathways that can guide future model design and grounding objectives.

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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Global Prior Meets Local Consistency: Dual-Memory Augmented Vision-Language-Action Model for Efficient Robotic Manipulation

Li, Zaijing, Hu, Bing, Shao, Rui, Chen, Gongwei, Jiang, Dongmei, Xie, Pengwei, Hao, Jianye, Nie, Liqiang

原文摘要

Hierarchical Vision-Language-Action (VLA) models have rapidly become a dominant paradigm for robotic manipulation. It typically comprising a Vision-Language backbone for perception and understanding, together with a generative policy for action generation. However, its performance is increasingly bottlenecked by the action generation proceess. (i) Low inference efficiency. A pronounced distributional gap between isotropic noise priors and target action distributions, which increases denoising steps and the incidence of infeasible samples. (ii) Poor robustness. Existing policies condition solely on the current observation, neglecting the constraint of history sequence and thus lacking awareness of task progress and temporal consistency. To address these issues, we introduce OptimusVLA, a dual-memory VLA framework with Global Prior Memory (GPM) and Local Consistency Memory (LCM). GPM replaces Gaussian noise with task-level priors retrieved from semantically similar trajectories, thereby shortening the generative path and reducing the umber of function evaluations (NFE). LCM dynamically models executed action sequence to infer task progress and injects a learned consistency constraint that enforces temporal coherence and smoothness of trajectory. Across three simulation benchmarks, OptimusVLA consistently outperforms strong baselines: it achieves 98.6% average success rate on LIBERO, improves over pi_0 by 13.5% on CALVIN, and attains 38% average success rate on RoboTwin 2.0 Hard. In Real-World evaluation, OptimusVLA ranks best on Generalization and Long-horizon suites, surpassing pi_0 by 42.9% and 52.4%, respectively, while delivering 2.9x inference speedup.

cs.CV

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

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Towards Stable Federated Continual Test-Time Adaptation in Wild World

Wang, Liwen, Dong, Xingbo, Liao, Iman Yi, Jin, Zhe

原文摘要

Federated Learning (FL) enables collaborative model training while preserving privacy, but faces challenges with client data heterogeneity and domain shifts during deployment. Although Personalized Federated Learning (PFL) mitigates heterogeneity, it typically requires labelled data from target clients, which is an impractical assumption. Test-Time Adaptation (TTA) offers label-free adaptation, yet its direct use in a continual federated setting risks destabilizing the global model and causing catastrophic forgetting. To address this, we consider the Federated Continual Test-Time Adaptation (FedCTTA) setting, where unlabeled clients arrive sequentially, requiring online adaptation and continuous global model updates. We propose BPFedCTTA, a framework that employs Bayesian Prior-guided Adaptation (BPA) for stable local adaptation via Maximum a Posteriori estimation, and Uncertainty-Gated Single-client Aggregation (UGSA) to selectively integrate updates based on client uncertainty. This approach balances adaptation with knowledge retention, thereby mitigating forgetting. Extensive experiments on cross-domain classification and segmentation show BPFedCTTA outperforms existing FL, PFL, and TTA methods in sequential adaptation and global model improvement. The source code will be made public upon acceptance.

cs.CV

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

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VirtueBench: Evaluating Trustworthiness under Uncertainty in Long Video Understanding

Yu, Xueqing, Li, Bohan, Li, Yan, Yang, Zhenheng

原文摘要

Recent Vision-Language Models (VLMs) have made remarkable progress in multimodal understanding tasks, yet their evaluation on long video understanding remains unreliable. Due to limited frame inputs, key frames necessary for answering the question may be missing from the model's input. However, models that truthfully refuse to answer under such uncertainty are marked as incorrect, while those that guess may coincidentally produce the correct answer and thus obtain deceptively higher accuracy, leading to misleading evaluation results and encouraging models to guess rather than respond honestly. To address this issue, we introduce VirtueBench, a benchmark explicitly designed to assess model trustworthiness under uncertainty. VirtueBench constructs multiple frame-sampling levels for each video and provides ground truths that distinguish between answerable and unanswerable cases. Evaluations on 25 open-source and commercial VLMs reveal distinct refusal behaviors across different model families, with refusal accuracy ranging from over 70% in the best models to nearly 0% in the worst. Moreover, most models exhibit a substantial drop in refusal when the prompt does not explicitly require them to do so. These findings highlight the need for developing trustworthy VLMs for multimodal understanding, guided by benchmarks and leaderboards that emphasize reliability and trustworthiness.

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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No Calibration, No Depth, No Problem: Cross-Sensor View Synthesis with 3D Consistency

Wu, Cho-Ying, Huang, Zixun, Huang, Xinyu, Ren, Liu

原文摘要

We present the first study of cross-sensor view synthesis across different modalities. We examine a practical, fundamental, yet widely overlooked problem: getting aligned RGB-X data, where most RGB-X prior work assumes such pairs exist and focuses on modality fusion, but it empirically requires huge engineering effort in calibration. We propose a match-densify-consolidate method. First, we perform RGB-X image matching followed by guided point densification. Using the proposed confidence-aware densification and self-matching filtering, we attain better view synthesis and later consolidate them in 3D Gaussian Splatting (3DGS). Our method uses no 3D priors for X-sensor and only assumes nearly no-cost COLMAP for RGB. We aim to remove the cumbersome calibration for various RGB-X sensors and advance the popularity of cross-sensor learning by a scalable solution that breaks through the bottleneck in large-scale real-world RGB-X data collection.

cs.CV

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

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Event-Illumination Collaborative Low-light Image Enhancement with a High-resolution Real-world Dataset

Xu, Senyan, Sun, Zhijing, Liu, Kean, Lu, Xin, Jiang, Ruixuan, Fu, Xueyang, Zha, Zheng-Jun

原文摘要

Event-based low-light image enhancement (LIE) methods mainly focus on incorporating high dynamic range (HDR) information from events while overlooking the essential global illumination in images and the inherent noise sensitivity of event signals in real-world scenarios. To address these issues, we propose EIC-LIE, an event-illumination collaborative LIE framework. Concretely, we first design an Event-Illumination Collaborative Interaction (EICI) module, which contains two key processes: forward gathering, which gathers HDR features across varying lighting conditions, and backward injection, which provides complementary content for illumination and event representations.Next, we introduce an Illumination-aware Event Filter (IAEF) that dynamically reduces event noise based on brightness statistics derived from images. Additionally, we build a beam-splitter-based hybrid imaging system to collect high-quality event-image pairs with temporal synchronization from dynamic scenes, providing the first high-resolution, real-world event-based LIE dataset.Extensive experiments show that our EIC-LIE outperforms state-of-the-art methods on five real-world and synthetic datasets, significantly surpassing previous methods with improvements of up to 1.24dB in PSNR and 0.069 in SSIM. The code and dataset are released at https://github.com/QUEAHREN/EIC-LIE.

cs.CV

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

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VMD-FACT: A New Video Dataset and MLLM-based method for Detecting Realistic AI-Generated Video Misinformation

Zhang, Yongkang, She, Dongyu, Ji, Baiyu, Geng, Qichuan, Zhou, Zhong, Wang, Yan

原文摘要

The rapid evolution of generative AI, including such models as Sora, has intensified the threat of video misinformation. A critical challenge in detecting these AI-generated video misinformation lies in a fundamental disconnect between existing datasets and practical deception tactics. Current datasets often disrupt cross-modal consistency through editing techniques, resulting in unrealistic and easily detectable artifacts. By contrast, generative video misinformation strives for semantic consistency across modalities to remain realism. To address this gap, we introduce RAVM: the first Realistic AI-Generated Video Misinformation Detection Dataset. Unlike existing Video Misinformation Detection (VMD) datasets that are limited to single-source manipulations, RAVM encompasses multiple manipulation sources--Claim, Video, Audio, and Cross-Modal Manipulation--each incorporating diverse manipulation techniques to generate realistic AI-generated video misinformation. To achieve this, we introduce an agent-driven framework for generating realistic video misinformation. Furthermore, we propose an IEEG model that represents multimodal evidence, fact-checking results, and their dependencies as an evidence graph for interpretable detection of AI-generated video misinformation. Extensive experiments on RAVM reveal the vulnerability of existing Multimodal Large Language Models (MLLMs) in detecting AI-generated video misinformation, while the proposed IEEG achieves state-of-the-art performance on RAVM. The dataset is publicly available at: https://gitee.com/VR_NAVE/ravm

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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