Image and Vision Computing最新文献_第7页

A robust and secure video recovery scheme with deep compressive sensing 基于深度压缩感知的鲁棒安全视频恢复方案

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-02-01 Epub Date: 2025-12-01 DOI: 10.1016/j.imavis.2025.105853

Jagannath Sethi , Jaydeb Bhaumik , Ananda S. Chowdhury

{"title":"A robust and secure video recovery scheme with deep compressive sensing","authors":"Jagannath Sethi , Jaydeb Bhaumik , Ananda S. Chowdhury","doi":"10.1016/j.imavis.2025.105853","DOIUrl":"10.1016/j.imavis.2025.105853","url":null,"abstract":"<div><div>In this paper, we propose a secure high quality video recovery scheme which can be useful for diverse applications like telemedicine and cloud-based surveillance. Our solution consists of deep learning-based video Compressive Sensing (CS) followed by a strategy for encrypting the compressed video. We split a video into a number of Groups Of Pictures (GOPs), where, each GOP consists of both keyframes and non-keyframes. The proposed video CS method uses a convolutional neural network (CNN) with a Structural Similarity Index Measure (SSIM) based loss function. Our recovery process has two stages. In the initial recovery stage, CNN is employed to make efficient use of spatial redundancy. In the deep recovery stage, non-keyframes are compensated by utilizing both keyframes and neighboring non-keyframes. Keyframes use multilevel feature compensation, and neighboring non-keyframes use single-level feature compensation. Additionally, we propose an unpredictable and complex chaotic map, with a broader chaotic range, termed as Sine Symbolic Chaotic Map (SSCM). For encrypting compressed features, we suggest a secure encryption scheme consisting of four operations: Forward Diffusion, Substitution, Backward Diffusion, and XORing with SSCM based chaotic sequence. Through extensive experimentation, we establish the efficacy of our combined solution over i) several state-of-the-art image and video CS methods, and ii) a number of video encryption techniques.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105853"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685309","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

Preserving instance-level characteristics for multi-instance generation 为多实例生成保留实例级特征

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-02-01 Epub Date: 2025-11-28 DOI: 10.1016/j.imavis.2025.105851

Jaehak Ryu , Sungwon Moon , Donghyeon Cho

{"title":"Preserving instance-level characteristics for multi-instance generation","authors":"Jaehak Ryu , Sungwon Moon , Donghyeon Cho","doi":"10.1016/j.imavis.2025.105851","DOIUrl":"10.1016/j.imavis.2025.105851","url":null,"abstract":"<div><div>Recently, there have been efforts to explore instance-level control in diffusion models, where multiple instances are generated independently and then integrated into a single scene. However, several issues arise when instances are closely positioned or overlapping. First, independently generated instances frequently differ in style and lack coherence, leading to changes in their attributes as they influence each other when merged. Second, instances often merge with one another or become absorbed into others. To tackle these challenges, we propose a local latent refinement (LLR) that enforces each local latent to meet its conditions and remain distinct from others. We also propose a local latent injection (LLI) method that gradually integrates local latents during global latent generation for smoother fusion. Also, we find that the variance of latents changes significantly after instance fusion, which greatly impacts the quality of the generated images. To remedy this, we apply an instance normalization layer to regulate the variance of the fused latents, thereby producing high-quality images. Extensive experiments demonstrate that our approach achieves both high fidelity in instance layout and superior image quality, even in cases of high overlap among instances.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105851"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618474","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 skin cancer classification with Soft Attention and genetic algorithm-optimized ensemble learning 基于软注意和遗传算法优化的集成学习增强皮肤癌分类

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-02-01 Epub Date: 2025-12-03 DOI: 10.1016/j.imavis.2025.105848

Vibhav Ranjan, Kuldeep Chaurasia, Jagendra Singh

{"title":"Enhancing skin cancer classification with Soft Attention and genetic algorithm-optimized ensemble learning","authors":"Vibhav Ranjan, Kuldeep Chaurasia, Jagendra Singh","doi":"10.1016/j.imavis.2025.105848","DOIUrl":"10.1016/j.imavis.2025.105848","url":null,"abstract":"<div><div>Skin cancer detection is a critical task in dermatology, where early diagnosis can significantly improve patient outcomes. In this work, we propose a novel approach for skin cancer classification that combines three deep learning models—InceptionResNetV2 with Soft Attention (SA), ResNet50V2 with SA, and DenseNet201—optimized using a Genetic Algorithm (GA) to find the best ensemble weights. The approach integrates several key innovations: Sigmoid Focal Cross-entropy Loss to address class imbalance, Mish activation for improved gradient flow, and Cosine Annealing learning rate scheduling for enhanced convergence. The GA-based optimization fine-tunes the ensemble weights to maximize classification performance, especially for challenging skin cancer types like melanoma. Experimental results on the HAM10000 dataset demonstrate the effectiveness of the proposed ensemble model, achieving superior accuracy and precision compared to individual models. This work offers a robust framework for skin cancer detection, combining state-of-the-art deep learning techniques with an optimization strategy.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105848"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737138","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

On the relevance of patch-based extraction methods for monocular depth estimation 基于斑块的提取方法在单目深度估计中的相关性研究

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-02-01 Epub Date: 2025-12-03 DOI: 10.1016/j.imavis.2025.105857

Pasquale Coscia, Antonio Fusillo, Angelo Genovese, Vincenzo Piuri, Fabio Scotti

{"title":"On the relevance of patch-based extraction methods for monocular depth estimation","authors":"Pasquale Coscia, Antonio Fusillo, Angelo Genovese, Vincenzo Piuri, Fabio Scotti","doi":"10.1016/j.imavis.2025.105857","DOIUrl":"10.1016/j.imavis.2025.105857","url":null,"abstract":"<div><div>Scene geometry estimation from images plays a key role in robotics, augmented reality, and autonomous systems. In particular, Monocular Depth Estimation (MDE) focuses on predicting depth using a single RGB image, avoiding the need for expensive sensors. State-of-the-art approaches use deep learning models for MDE while processing images as a whole, sub-optimally exploiting their spatial information. A recent research direction focuses on smaller image patches, as depth information varies across different regions of an image. This approach reduces model complexity and improves performance by capturing finer spatial details. From this perspective, we propose a novel warp patch-based extraction method which corrects perspective camera distortions, and employ it in tailored training and inference pipelines. Our experimental results show that our patch-based approach outperforms its full-image-trained counterpart and the classical crop patch-based extraction. With our technique, we obtain a general performance enhancements over recent state-of-the-art models. Code is available at <span><span>https://github.com/AntonioFusillo/PatchMDE</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105857"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737139","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

SEAGNet: Spatial–Epipolar–Angular–Global feature learning for light field super-resolution 面向光场超分辨率的空间-极-角-全局特征学习

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-02-01 Epub Date: 2025-12-13 DOI: 10.1016/j.imavis.2025.105866

Xingzheng Wang, Haotian Zhang, Yuhang Lin, Yuanbo Huang, Jiahao Lin

{"title":"SEAGNet: Spatial–Epipolar–Angular–Global feature learning for light field super-resolution","authors":"Xingzheng Wang, Haotian Zhang, Yuhang Lin, Yuanbo Huang, Jiahao Lin","doi":"10.1016/j.imavis.2025.105866","DOIUrl":"10.1016/j.imavis.2025.105866","url":null,"abstract":"<div><div>In light field (LF) image super-resolution (SR), comprehensive learning of LF information is crucial for accurately recovering image details. Because 4D LF structures are so complex, current methods usually use special convolutions and modules to separately extract different LF characteristics (like spatial, angular, and EPI features) before combining them. But these methods focus too much on local LF information and not enough on global 4D LF features. This makes it hard for them to get better. To overcome this issue, we suggest a straightforward yet effective Global Feature Extraction Module (GFEM). This module extracts the global information from the entire 4D light field. Our method does this by using all of these features together. We also introduce a tool called the Progressive Angular Feature Extractor (PAFE), which gradually expands the area that extracts features to make sure it can extract features at different angles. We also designed a Spatial Gated Feed-forward Network (SGFN) to replace the standard feed-forward network in Transformer. This has resulted in our new Intra-Transformer architecture, which optimizes feature flow and enhances local detail extraction. We did a lot of experiments on different public datasets, and these showed that our method is better than other methods that are currently available.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105866"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790255","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

Multi-modal cooperative fusion network for dual-stream RGB-D salient object detection 双流RGB-D显著目标检测的多模态协同融合网络

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-02-01 Epub Date: 2025-11-19 DOI: 10.1016/j.imavis.2025.105835

Jingyu Wu , Fuming Sun , Haojie Li , Mingyu Lu

{"title":"Multi-modal cooperative fusion network for dual-stream RGB-D salient object detection","authors":"Jingyu Wu , Fuming Sun , Haojie Li , Mingyu Lu","doi":"10.1016/j.imavis.2025.105835","DOIUrl":"10.1016/j.imavis.2025.105835","url":null,"abstract":"<div><div>Most existing RGB-D salient object detection tasks use convolution operations to design complex fusion modules for cross-modal information fusion. How to correctly integrate RGB and depth features into multi-modal features is important to salient object detection (SOD). Due to the differences between different modal features, the salient object detection model is seriously hindered in achieving better performance. To address the issues mentioned above, we design a multi-modal cooperative fusion network (MCFNet) to achieve RGB-D SOD. Firstly, we propose an edge feature refinement module to remove interference information in shallow features and improve the edge accuracy of SOD. Secondly, a depth optimization module is designed to optimize erroneous estimates in the depth maps, which effectively reduces the impact of noise and improves the performance of the model. Finally, we construct a progressive fusion module that gradually integrates RGB and depth features in a layered manner to achieve an efficient fusion of cross-modal features. Experimental results on six datasets show that our MCFNet performs better than other state-of-the-art (SOTA) methods, which provide new ideas for salient object detection tasks.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105835"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685264","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

Attention and mask-guided context fusion network for camouflaged object detection 用于伪装目标检测的注意力和面具引导上下文融合网络

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-02-01 Epub Date: 2025-12-22 DOI: 10.1016/j.imavis.2025.105887

Qiuying Han , Shaohui Zhang , Peng Wang

{"title":"Attention and mask-guided context fusion network for camouflaged object detection","authors":"Qiuying Han , Shaohui Zhang , Peng Wang","doi":"10.1016/j.imavis.2025.105887","DOIUrl":"10.1016/j.imavis.2025.105887","url":null,"abstract":"<div><div>Camouflaged Object Detection (COD) aims to accurately identify objects visually embedded in their surroundings, which is considerably more challenging than conventional object detection due to low contrast, complex backgrounds, and varying object scales. Although recent deep learning approaches have shown promising results, they often suffer from incomplete or inaccurate detections, primarily due to the inadequate exploitation of multi-scale contextual features and cross-level information. To address these limitations, we propose a novel architecture, termed Attention and Mask-guided Context Fusion Network (AMCFNet). The framework comprises two core modules: Attentional Multi-scale Context Aggregation (AMCA) and Mask-guided Cross-level Fusion (MCF). The AMCA module improves the semantic representation of features at different levels by merging both global and local context information through a bidirectional attention mechanism, which includes wavelet-based modulation of channel and spatial data. The MCF module leverages high-level mask priors to guide the fusion of semantic and spatial features, applying an attention-weighted mechanism to highlight object-related regions while minimizing background interference. Comprehensive tests on four well-known COD benchmark datasets show that AMCFNet outperforms existing methods, providing more accurate camouflaged object detection under various challenging conditions.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"166 ","pages":"Article 105887"},"PeriodicalIF":4.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840003","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

MOT-STM: Maritime Object Tracking: A Spatial-Temporal and Metadata-based approach 海事目标跟踪：一种基于时空和元数据的方法

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-01-01 Epub Date: 2025-11-12 DOI: 10.1016/j.imavis.2025.105826

Vinayak S. Nageli , Arshad Jamal , Puneet Goyal , Rama Krishna Sai S Gorthi

{"title":"MOT-STM: Maritime Object Tracking: A Spatial-Temporal and Metadata-based approach","authors":"Vinayak S. Nageli , Arshad Jamal , Puneet Goyal , Rama Krishna Sai S Gorthi","doi":"10.1016/j.imavis.2025.105826","DOIUrl":"10.1016/j.imavis.2025.105826","url":null,"abstract":"<div><div>Object Tracking and Re-Identification (Re-ID) in maritime environments using drone video streams presents significant challenges, especially in search and rescue operations. These challenges mainly arise from the small size of objects from high drone altitudes, sudden movements of the drone’s gimbal and limited appearance diversity of objects. The frequent occlusion in these challenging conditions makes Re-ID difficult in long-term tracking.</div><div>In this work, we present a novel framework, Maritime Object Tracking with Spatial–Temporal and Metadata-based modeling (MOT-STM), designed for robust tracking and re-identification of maritime objects in challenging environments. The proposed framework adapts multi-resolution spatial feature extraction using Cross-Stage Partial with Full-Stage (C2FDark) backbone combined with temporal modeling via Video Swin Transformer (VST), enabling effective spatio-temporal representation. This design enhances detection and significantly improves tracking performance in the maritime domain.</div><div>We also propose a metadata-driven Re-ID module named Metadata-Assisted Re-ID (MARe-ID), which leverages drone’s metadata such as Global Positioning System (GPS) coordinates, altitude and camera orientation to enhance long-term tracking. Unlike traditional appearance-based Re-ID, MARe-ID remains effective even in scenarios with limited visual diversity among the tracked objects and is generic enough to be integrated into any State-of-the-Art (SotA) multi-object tracking framework as a Re-ID module.</div><div>Through extensive experiments on the challenging SeaDronesSee dataset, we demonstrate that MOT-STM significantly outperforms existing methods in maritime object tracking. Our approach achieves a state-of-the-art performance attaining a HOTA score of 70.14% and an IDF1 score of 88.70%, showing the effectiveness and robustness of the proposed MOT-STM framework.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"165 ","pages":"Article 105826"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520880","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

FaceMINT: A library for gaining insights into biometric face recognition via mechanistic interpretability FaceMINT：一个通过机制可解释性获得生物特征面部识别见解的库

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-01-01 Epub Date: 2025-11-07 DOI: 10.1016/j.imavis.2025.105804

Peter Rot , Robert Jutreša , Peter Peer , Vitomir Štruc , Walter Scheirer , Klemen Grm

{"title":"FaceMINT: A library for gaining insights into biometric face recognition via mechanistic interpretability","authors":"Peter Rot , Robert Jutreša , Peter Peer , Vitomir Štruc , Walter Scheirer , Klemen Grm","doi":"10.1016/j.imavis.2025.105804","DOIUrl":"10.1016/j.imavis.2025.105804","url":null,"abstract":"<div><div>Deep-learning models, including those used in biometric recognition, have achieved remarkable performance on benchmark datasets as well as real-world recognition tasks. However, a major drawback of these models is their lack of transparency in decision-making. Mechanistic interpretability has emerged as a promising research field intended to help us gain insights into such models, but its application to biometric data remains limited. In this work, we bridge this gap by introducing the FaceMINT library, a publicly available Python library (build on top of Pytorch) that enables biometric researchers to inspect their models through mechanistic interpretability. It provides a plug-and-play solution that allows researchers to seamlessly switch between the analyzed biometric models, evaluate state-of-the-art sparse autoencoders, select from various image parametrizations, and fine-tune hyperparameters. Using a large scale Glint360K dataset, we demonstrate the usability of FaceMINT by applying its functionality to two state-of-the-art (deep-learning) face recognition models: AdaFace, based on Convolutional Neural Networks (CNN), and SwinFace, based on transformers. The proposed library implements various sparse auto-encoders (SAEs), including vanilla SAE, Gated SAE, JumpReLU SAE, and TopK SAE, which have achieved state-of-the-art results in the mechanistic interpretability of large language models. Our study highlights the promise of mechanistic interpretability in the biometric field, providing new avenues for researchers to explore model transparency and refine biometric recognition systems. The library is publicly available at <span><span>www.gitlab.com/peterrot/facemint</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"165 ","pages":"Article 105804"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145521234","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

Enhanced skeleton-based Group Activity Recognition through spatio-temporal graph convolution with cross-dimensional attention 基于跨维注意的时空图卷积增强基于骨骼的群体活动识别

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-01-01 Epub Date: 2025-10-30 DOI: 10.1016/j.imavis.2025.105784

Dongli Wang , Yongcan Weng , Xiaolin Zhu , Yan Zhou , Zixin Zhang , Richard Irampaye

{"title":"Enhanced skeleton-based Group Activity Recognition through spatio-temporal graph convolution with cross-dimensional attention","authors":"Dongli Wang , Yongcan Weng , Xiaolin Zhu , Yan Zhou , Zixin Zhang , Richard Irampaye","doi":"10.1016/j.imavis.2025.105784","DOIUrl":"10.1016/j.imavis.2025.105784","url":null,"abstract":"<div><div>Group Activity Recognition is a pivotal task in video understanding, with broad applications ranging from surveillance to human–computer interaction. Traditional RGB-based methods face challenges such as privacy concerns, environmental sensitivity, and fragmented scene-level semantic understanding. Skeleton-based approaches offer a promising alternative but often suffer from limited exploration of heterogeneous features and the absence of explicit modeling for human-object interactions. In this paper, we introduce a lightweight framework for skeleton-based GAR, leveraging an attention-enhanced spatio-temporal graph convolutional network. Specially, we first decouple joint and bone features along with their motion patterns, constructing a global human-object relational graph using an attention graph convolution module (AGCM). Additionally, we incorporate a Multi-Scale Temporal Convolution Module (MTC) and a Cross-Dimensional Attention Module (CDAM) to dynamically focus on key spatio-temporal nodes and feature channels. Our method achieves significant improvements in accuracy while maintaining high computational efficiency, making it suitable for real-time applications in privacy-sensitive scenarios. Experiments on the Volleyball and NBA datasets demonstrate that our method achieves competitive performance using only skeleton input, significantly reducing parameters and computational cost compared to mainstream approaches. Here, our method show an improvement in Multi-Class Per-Class Accuracy (MPCA) to 96.1% on the Volleyball dataset and 71.6% on the NBA dataset, offering a lightweight and efficient solution for GAR in privacy-sensitive scenarios.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"165 ","pages":"Article 105784"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145521219","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