Computer Vision and Image Understanding最新文献_第5页

QB-MOTR: A simple query bootstrapping end-to-end multi-object tracking method with transformer QB-MOTR：一个简单的带变压器的查询引导端到端多目标跟踪方法

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-03-01 Epub Date: 2026-02-17 DOI: 10.1016/j.cviu.2026.104682

Zifan Han , Xuchong Zhang , Hang Wang , Hongbin Sun

{"title":"QB-MOTR: A simple query bootstrapping end-to-end multi-object tracking method with transformer","authors":"Zifan Han , Xuchong Zhang , Hang Wang , Hongbin Sun","doi":"10.1016/j.cviu.2026.104682","DOIUrl":"10.1016/j.cviu.2026.104682","url":null,"abstract":"<div><div>Tracking-by-query based multi-object-tracking (MOT) aims to simplify the complicated and tiresome post-processing of the traditional tracking-by-detection paradigm in an end-to-end manner. However, the former method usually suffers from the conflict between detection and association due to the semantic ambiguity between tracking and detection instances in joint training, resulting in unsatisfactory performance compared to the latter method. Previous tracking-by-query methods usually use an extra detector to decouple the detection and association tasks. However, these methods inevitably introduce complex operations like additional detectors or manual hyperparameters adjustment. In this paper, we propose a simple end-to-end MOT method, <strong>Q</strong>uery <strong>B</strong>oostrapping <strong>M</strong>ulti-<strong>O</strong>bject <strong>T</strong>racking with T<strong>R</strong>ansformer (<strong>QB-MOTR</strong>) to alleviate the conflict. Specifically, a Query Boostarpping module is designed to enhance the semantic features of the tracking query in order to distinguish the detection and tracking instances. This module integrates both positional and specific semantic information into the tracker effectively while maintaining the simple pipeline of the whole network. The tracking performance of various MOT networks is evaluated on multiple datasets. Evaluation results demonstrate that QB-MOTR surpasses baseline method MOTR by about 18.1%. Besides, the detection and association performance is superior to the state-of-the-art end-to-end method MeMOTR with much simpler training and inference pipeline.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"265 ","pages":"Article 104682"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147422111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

OVGrasp: Open-Vocabulary Intent Detection for Grasping Assistance using ExoGlove OVGrasp：使用ExoGlove的开放词汇意图检测辅助抓取

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1016/j.cviu.2026.104676

Chen Hu , Shan Luo , Letizia Gionfrida

{"title":"OVGrasp: Open-Vocabulary Intent Detection for Grasping Assistance using ExoGlove","authors":"Chen Hu , Shan Luo , Letizia Gionfrida","doi":"10.1016/j.cviu.2026.104676","DOIUrl":"10.1016/j.cviu.2026.104676","url":null,"abstract":"<div><div>Grasping assistance is essential for restoring autonomy in individuals with motor impairments, particularly in unstructured environments where object categories and user intentions are diverse and unpredictable. We present <strong>OVGrasp</strong>, a hierarchical control framework for grasp assistance that integrates RGB-D vision, open vocabulary prompts, and voice commands to enable robust multimodal interaction. To enhance generalisation in open environments, OVGrasp incorporates a vision language foundation model with an open vocabulary mechanism, which enables zero-shot detection of previously unseen objects without retraining. A multimodal decision maker further fuses spatial and linguistic cues to infer user intent, such as grasp or release, in situations involving multiple objects. We deploy the complete framework on a custom egocentric view wearable exoskeleton and conduct systematic evaluations on fifteen objects across three grasp types. Experimental results with ten participants show that OVGrasp achieves a grasping ability score (GAS) of 87.00%, surpassing existing baselines and providing improved kinematic alignment with natural hand movement.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"265 ","pages":"Article 104676"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SpectraDiff: Enhancing the fidelity of Infrared Image Translation with object-aware diffusion SpectraDiff：利用目标感知扩散增强红外图像平移的保真度

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-03-01 Epub Date: 2026-02-26 DOI: 10.1016/j.cviu.2026.104709

Incheol Park , Youngwan Jin , Nalcakan Yagiz , Hyeongjin Ju , Sanghyeop Yeo , Shiho Kim

{"title":"SpectraDiff: Enhancing the fidelity of Infrared Image Translation with object-aware diffusion","authors":"Incheol Park , Youngwan Jin , Nalcakan Yagiz , Hyeongjin Ju , Sanghyeop Yeo , Shiho Kim","doi":"10.1016/j.cviu.2026.104709","DOIUrl":"10.1016/j.cviu.2026.104709","url":null,"abstract":"<div><div>Autonomous systems commonly rely on RGB cameras, which are susceptible to failure in low-light and adverse conditions. Infrared (IR) imaging provides a viable alternative by capturing thermal signatures independent of visible illumination. However, its high cost and integration complexities limit widespread adoption. To address these challenges, we introduce SpectraDiff, a diffusion-based framework that synthesizes realistic IR images by fusing RGB inputs with refined semantic segmentation. Through our RGB-Seg Object-Aware (RSOA) module, SpectraDiff learns object-specific IR intensities by leveraging object-aware features. The SpectraDiff architecture, featuring a novel Spectral Attention Block, enforces self-attention among semantically similar pixels while leveraging cross-attention with the original RGB to preserve high-frequency details. Extensive evaluations on FLIR, FMB, MFNet, IDD-AW, and RANUS demonstrate SpectraDiff’s superior performance over existing methods, as measured by both perceptual (FID, LPIPS, DISTS) and fidelity (SSIM, SAM) metrics. Code and pretrained models are available at: <span><span>https://yonsei-stl.github.io/SpectraDiff/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"266 ","pages":"Article 104709"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147426834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

KASS: Efficient video artifact removal via Kernel-Adaptive Spatiotemporal Synchronization 基于核自适应时空同步的高效视频伪影去除

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1016/j.cviu.2026.104649

Liqun Lin, Fawei Tang, Mingxing Wang, Yipeng Liao, Tiesong Zhao

{"title":"KASS: Efficient video artifact removal via Kernel-Adaptive Spatiotemporal Synchronization","authors":"Liqun Lin, Fawei Tang, Mingxing Wang, Yipeng Liao, Tiesong Zhao","doi":"10.1016/j.cviu.2026.104649","DOIUrl":"10.1016/j.cviu.2026.104649","url":null,"abstract":"<div><div>Video compression is essential for reducing bandwidth and storage demands, but often introduces artifacts that impair visual quality. Current Video Compression Artifact Removal (VCAR) methods face challenges including high computational complexity and unstable enhancement performance. To address these issues, we propose a novel Kernel-Adaptive Spatiotemporal Synchronization (KASS) network. First, a Dual-branch Alignment Module (DAM) enables multi-receptive-field feature alignment for modeling complex motion patterns. Second, an Adaptive Spatial Attention (ASA) block employs multi-branch deformable convolution with varying kernel sizes to locate artifacts. It then restores high-frequency details efficiently through attention-guided reconstruction. Third, a Spatiotemporal Multi-scale Alignment (SMA) block captures global spatiotemporal information and integrates multi-frame features via spatial and channel attention. This design effectively removes artifacts while improving alignment and enhancement stability. Experiments demonstrate that KASS significantly improves artifact removal performance while overcoming key limitations in alignment accuracy, computational burden, and enhancement stability.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"265 ","pages":"Article 104649"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AnomalySD: One-for-all few-shot anomaly detection via pre-trained diffusion models AnomalySD：通过预先训练的扩散模型进行一次性的少量异常检测

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1016/j.cviu.2026.104668

Zhenyu Yan, Qingqing Fang, Wenxi Lv, Qinliang Su

{"title":"AnomalySD: One-for-all few-shot anomaly detection via pre-trained diffusion models","authors":"Zhenyu Yan, Qingqing Fang, Wenxi Lv, Qinliang Su","doi":"10.1016/j.cviu.2026.104668","DOIUrl":"10.1016/j.cviu.2026.104668","url":null,"abstract":"<div><div>Anomaly detection is a critical task in industrial manufacturing, aiming to identify defective parts of products. Most industrial anomaly detection methods assume the availability of sufficient normal data for training. This assumption may not hold true due to the cost of labeling or data privacy policies. Additionally, mainstream methods require training bespoke models for different objects, which incurs heavy costs and lacks flexibility in practice. To address these issues, in this paper, we for the first time propose to leverage the pretrained generative model, Stable Diffusion (SD), to perform the one-for-all few-shot anomaly detection task, in contrast to existing few-shot anomaly detection works that heavily rely on the use of pre-trained representation-based CLIP model. To adapt SD to anomaly detection task, we design different hierarchical text descriptions and the foreground mask mechanism for fine-tuning the SD. At the testing stage, to accurately mask anomalous regions for inpainting, we propose a multi-scale mask strategy and prototype-guided mask strategy to handle diverse anomalous regions. Hierarchical text prompts are also utilized to guide the process of inpainting in the inference stage. Extensive experiments on the MVTec-AD and VisA datasets demonstrate the superiority of our approach. We achieved anomaly classification and segmentation results of 93.6%/94.8% AUROC on the MVTec-AD dataset and 86.1%/96.5% AUROC on the VisA dataset under multi-class and one-shot settings. The source code of our method is available at <span><span>https://github.com/YanZhenyu1999/AnomalySD.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"265 ","pages":"Article 104668"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing workplace safety through assistive computer vision: Real-time hazard recognition using the Workplace Hazards Dataset (WHD) 利用辅助电脑视觉加强工作场所安全：利用工作场所危害数据集进行实时危害识别

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI: 10.1016/j.cviu.2026.104681

Masoud Ayoubi, Mehrdad Arashpour

{"title":"Enhancing workplace safety through assistive computer vision: Real-time hazard recognition using the Workplace Hazards Dataset (WHD)","authors":"Masoud Ayoubi, Mehrdad Arashpour","doi":"10.1016/j.cviu.2026.104681","DOIUrl":"10.1016/j.cviu.2026.104681","url":null,"abstract":"<div><div>Assistive computer vision technologies have the potential to significantly enhance workplace safety by enabling early detection of hazards and supporting proactive risk management. However, the development of such systems is constrained by the absence of comprehensive video datasets and clearly defined tasks that capture real-world hazard conditions. This study formulates pre-incident hazard recognition as a distinct assistive-vision problem, focusing on identifying unsafe states that precede incidents rather than the incidents themselves. To address this problem, we propose the Workplace Hazards Dataset (WHD), a balanced and diverse set of real-world videos representing five universal hazard categories in varied workplace settings. Furthermore, we establish a standardized benchmarking framework that evaluates state-of-the-art convolutional and transformer-based video models on both performance and inference-latency metrics to assess real-time feasibility. Experimental results show that the Multiscale Vision Transformer (MViT 16 × 4) achieves the highest accuracy (74.1%) while maintaining efficient inference speed, highlighting the importance of balancing recognition accuracy with processing time. Overall, this work defines a new benchmark task for assistive computer vision and provides the foundation for developing real-time hazard recognition systems that enhance safety and efficiency in high-risk environments.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"265 ","pages":"Article 104681"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SRDR: Style recovery and detail replenishment matter for single image dehazing SRDR：风格恢复和细节补充对单幅图像去雾很重要

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-03-01 Epub Date: 2026-02-17 DOI: 10.1016/j.cviu.2026.104688

Yuehua Li, Songwei Pei, Wenzheng Yang, BingFeng Liu, Shuhuai Wang

{"title":"SRDR: Style recovery and detail replenishment matter for single image dehazing","authors":"Yuehua Li, Songwei Pei, Wenzheng Yang, BingFeng Liu, Shuhuai Wang","doi":"10.1016/j.cviu.2026.104688","DOIUrl":"10.1016/j.cviu.2026.104688","url":null,"abstract":"<div><div>Single image dehazing, as a representative low-level vision task, is of paramount importance in substantial applications such as object detection and autonomous driving. Nowadays, most image dehazing methods focus on directly learning the overall difference between hazy and clear image pairs, thus making the learning task excessively challenging and restricting the performance of image dehazing to a certain extent. In this paper, we draw on considerations about image style and content from the style transfer task, and believe that the degradation of hazy images typically involves the transition of style and the hiding of details, and then the image dehazing task can be divided and conquered according to the two aspects to reduce the learning difficulty of the dehazing network for effective image dehazing. Based on this inspiration, in this paper, we propose an image dehazing network with a specific focus on style recovery and detail replenishment, namely SRDR, which firstly recovers the style and extracts the detail of the hazy image, respectively, and then aggregates the information from style recovery and detail replenishment for better image dehazing. The SRDR mainly consists of three modules: Style Recovery Module (SRM), Detail Replenishment Module (DRM), and Cross Fusion Module (CFM). SRM is responsible for style recovery by adapting the pre-trained MAE model, DRM handles the detail replenishment with multiple direction convolutions, and CFM is an information aggregation module. Extensive experiments demonstrate that SRDR achieves state-of-the-art performance on numerous mainstream datasets.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"265 ","pages":"Article 104688"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147422112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Biometric technology roadmapping for personalized augmentative and alternative communication 个性化、增强和替代通信的生物识别技术路线图

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-03-01 Epub Date: 2026-01-22 DOI: 10.1016/j.cviu.2026.104664

Svetlana Yanushkevich , Eva Berepiki , Philip Ciunkiewicz , Vlad Shmerko , Gregor Wolbring , Richard Guest

引用次数: 0

Securing workers and workspaces: Contextual privacy for vision-based ergonomics 保护工人和工作空间：基于视觉的人体工程学的上下文隐私

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1016/j.cviu.2026.104675

Sander De Coninck , Emilio Gamba , Bart Van Doninck , Abdellatif Bey-Temsamani , Thorsten Cardoen , Sam Leroux , Pieter Simoens

{"title":"Securing workers and workspaces: Contextual privacy for vision-based ergonomics","authors":"Sander De Coninck , Emilio Gamba , Bart Van Doninck , Abdellatif Bey-Temsamani , Thorsten Cardoen , Sam Leroux , Pieter Simoens","doi":"10.1016/j.cviu.2026.104675","DOIUrl":"10.1016/j.cviu.2026.104675","url":null,"abstract":"<div><div>Multi-camera computer vision in industry offers advantages but poses risks to worker privacy and intellectual property through exposure of sensitive contextual information. Existing privacy methods often inadequately protect background details crucial in manufacturing. This issue is prominent in applications like automated ergonomic assessment, where visual data for posture analysis can reveal sensitive workplace information. We propose a system for simultaneous personal privacy and enhanced contextual intellectual property protection, featuring a novel probabilistic obfuscation technique. Our edge-based Generative Adversarial Privacy system employs a modified obfuscator that learns to inject controlled, pixel-wise random noise, particularly into non-critical background regions. This more effectively obscures IP-sensitive environmental details before data transmission for central analysis (e.g., pose estimation). Our approach, validated in a multi-camera ergonomic study, effectively protects worker privacy and contextual IP (metrics-evaluated) and maintains 3D pose accuracy for reliable ergonomic assessment. This work provides a solution for deploying vision systems in sensitive industrial settings by holistically addressing privacy requirements through an advanced, adaptive obfuscation strategy.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"265 ","pages":"Article 104675"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recent advances of local mechanisms in vision foundation models: A survey and outlook 视觉基础模型局部机制研究进展综述与展望

IF 3.5 3区计算机科学

Computer Vision and Image Understanding Pub Date : 2026-03-01 Epub Date: 2026-02-18 DOI: 10.1016/j.cviu.2026.104679

Qiangchang Wang , Jing Li , Yilong Yin , Huimin Lu

{"title":"Recent advances of local mechanisms in vision foundation models: A survey and outlook","authors":"Qiangchang Wang , Jing Li , Yilong Yin , Huimin Lu","doi":"10.1016/j.cviu.2026.104679","DOIUrl":"10.1016/j.cviu.2026.104679","url":null,"abstract":"<div><div>Vision foundation models (VFMs) play a crucial role in complex, real-world computer vision applications. Key developments such as Vision Transformers, self-supervised learning, and multi-modal learning have significantly contributed to the advancement of VFMs. However, these models face challenges, including high resource demands, weak locality, and a lack of generalized features. Drawing inspiration from the human brain’s ability to emphasize important information while suppressing irrelevant input, local mechanisms have been designed to address these challenges and enhance VFMs development. Local mechanisms not only focus on specific parts of an input to learn discriminative, fine-grained features but also selectively process information, thereby improving model efficiency. These mechanisms exhibit unique characteristics across different domains. In this survey, we provide a systematic review of local mechanisms across three key areas of VFMs: multi-modal learning, self-supervised learning, and Vision Transformers. We categorize the various local mechanisms, summarize quantitative results in each area, and analyze the advantages and disadvantages of each category in depth, offering insights for further exploration. We examine the potential of VFMs in semi-supervised learning, weakly supervised learning, domain adaptation, and few-shot learning, highlighting their specific applications in remote sensing and medical image analysis. Finally, we discuss potential research directions related to local mechanisms that could inspire future work. To the best of our knowledge, this is the first comprehensive survey on local mechanisms across different fields. We hope this review will provide valuable insights and guide future research in VFMs.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"265 ","pages":"Article 104679"},"PeriodicalIF":3.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147422113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0