Journal of Visual Communication and Image Representation最新文献_第2页

HierarchicalGeoCount: Hierarchical scale perception for zero-shot object counting in remote sensing HierarchicalGeoCount：用于遥感零拍摄目标计数的分层尺度感知

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-04-01 Epub Date: 2026-03-27 DOI: 10.1016/j.jvcir.2026.104793

Binyuan Huang, Jiayi Wang, Zhenzhong Chen

引用次数: 0

Stability optimization in action imitation for humanoid robot 仿人机器人动作模仿的稳定性优化

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-03-01 Epub Date: 2026-01-29 DOI: 10.1016/j.jvcir.2026.104738

Yi Lu, Shenghao Ren, Zhiyu Jin, Qiu Shen

引用次数: 0

Semantic Response GAN (SR-GAN) for embroidery pattern generation 语义响应GAN （SR-GAN）用于刺绣图案生成

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-03-01 Epub Date: 2026-01-08 DOI: 10.1016/j.jvcir.2026.104707

Shaofan Chen

引用次数: 0

LaDeL: Lane detection via multimodal large language model with visual instruction tuning LaDeL：通过带有视觉指令调整的多模态大语言模型进行车道检测

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-03-01 Epub Date: 2026-01-06 DOI: 10.1016/j.jvcir.2025.104704

Yun Zhang , Xin Cheng , Zhou Zhou , Jingmei Zhou , Tong Yang

{"title":"LaDeL: Lane detection via multimodal large language model with visual instruction tuning","authors":"Yun Zhang , Xin Cheng , Zhou Zhou , Jingmei Zhou , Tong Yang","doi":"10.1016/j.jvcir.2025.104704","DOIUrl":"10.1016/j.jvcir.2025.104704","url":null,"abstract":"<div><div>Lane detection plays a fundamental role in autonomous driving by providing geometric and semantic guidance for robust localization and planning. Empirical studies have shown that reliable lane perception can reduce vehicle localization error by up to 15% and improve trajectory stability by more than 10%, underscoring its critical importance in safety-critical navigation systems. Visual degradations such as occlusions, worn paint, and illumination shifts result in missing or ambiguous lane boundaries, reducing the reliability of appearance-only methods and motivating scene-aware reasoning. Inspired by the human ability to jointly interpret scene context and road structure, this work presents LaDeL (Lane Detection with Large Language Models), which, to our knowledge, is the first framework to leverage multimodal large language models for lane detection through visual-instruction reasoning. LaDeL reformulates lane perception as a multimodal question-answering task that performs lane localization, lane counting, and scene captioning in a unified manner. We introduce lane-specific tokens to enable precise numerical coordinate prediction and construct a diverse instruction-tuning corpus combining lane queries, lane-count prompts, and scene descriptions. Experiments demonstrate that LaDeL achieves state-of-the-art performance, including an F1-score of 82.35% on CULane and 98.23% on TuSimple, outperforming previous methods. Although LaDeL requires greater computational resources than conventional lane detection networks, it provides new insight into integrating geometric perception with high-level reasoning. Beyond lane detection, this formulation opens opportunities for language-guided perception and reasoning in autonomous driving, including road-scene analysis, interactive driving assistants, and language-aware perception.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"116 ","pages":"Article 104704"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Pedestrian trajectory prediction using multi-cue transformer 基于多线索变压器的行人轨迹预测

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-03-01 Epub Date: 2026-01-15 DOI: 10.1016/j.jvcir.2026.104723

Yanlong Tian , Rui Zhai , Xiaoting Fan , Qi Xue , Zhong Zhang , Xinshan Zhu

{"title":"Pedestrian trajectory prediction using multi-cue transformer","authors":"Yanlong Tian , Rui Zhai , Xiaoting Fan , Qi Xue , Zhong Zhang , Xinshan Zhu","doi":"10.1016/j.jvcir.2026.104723","DOIUrl":"10.1016/j.jvcir.2026.104723","url":null,"abstract":"<div><div>Pedestrian trajectory prediction is a challenging issue because the future trajectories are influenced by the surrounding environment and constrained by the common sense rules. The existing trajectory prediction methods typically consider one kind of cues, i.e., social-aware cue, environment-aware cue, and goal-conditioned cue to model the interactions with the trajectory information, which results in insufficient interactions. In this article, we propose an innovative Transformer network named Multi-cue Transformer (McTrans) aimed at pedestrian trajectory prediction, where we design the Hierarchical Cross-Attention (HCA) module to learn the goal–social–environment interactions between the trajectory information of pedestrians and three kinds of cues from the perspectives of temporal and spatial dependencies. Furthermore, in order to reasonably utilize the guidance of the goal information, we propose the Gradual Goal-guided Loss (GGLoss) which gradually increases the weights of the coordinate difference between the predicted goal and the ground-truth goal as the time steps increase. We conduct extensive experiments on three public datasets, i.e., SDD, inD, and ETH/UCY. The experimental results demonstrate that the proposed McTrans is superior to other state-of-the-art methods.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"116 ","pages":"Article 104723"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Global–local co-regularization network for facial action unit detection 面部动作单元检测的全局-局部协同正则化网络

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-03-01 Epub Date: 2026-01-21 DOI: 10.1016/j.jvcir.2026.104728

Yumei Tan , Haiying Xia , Shuxiang Song

{"title":"Global–local co-regularization network for facial action unit detection","authors":"Yumei Tan , Haiying Xia , Shuxiang Song","doi":"10.1016/j.jvcir.2026.104728","DOIUrl":"10.1016/j.jvcir.2026.104728","url":null,"abstract":"<div><div>Facial action unit (AU) detection poses challenges in capturing discriminative local features and intricate AU correlations. To solve this challenge, we propose an effective Global–local Co-regularization Network (Co-GLN) trained in a collaborative manner. Co-GLN consists a global branch and a local branch, aiming to establish global feature-level interrelationships in the global branch while excavating region-level discriminative features in the local branch. Specifically, in the global branch, a Global Interaction (GI) module is designed to enhance cross-pixel relations for capturing global semantic information. The local branch comprises three components: the Region Localization (RL) module, the Intra-feature Relation Modeling (IRM) module, and the Region Interaction (RI) module. The RL module extracts regional features according to the pre-defined facial regions, then IRM module extracts local features for each region. Subsequently, the RI module integrates complementary information across regions. Finally, a co-regularization constraint is used to encourage consistency between the global and local branches. Experimental results demonstrate that Co-GLN consistently enhances AU detection performance on the BP4D and DISFA datasets.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"116 ","pages":"Article 104728"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Infrared small UAV target detection via depthwise separable residual dense attention network 基于深度可分离残差密集注意网络的红外小型无人机目标检测

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-03-01 Epub Date: 2026-01-06 DOI: 10.1016/j.jvcir.2025.104703

Keyang Cheng , Nan Chen , Chang Liu , Yue Yu , Hao Zhou , Zhe Wang , Changsheng Peng

{"title":"Infrared small UAV target detection via depthwise separable residual dense attention network","authors":"Keyang Cheng , Nan Chen , Chang Liu , Yue Yu , Hao Zhou , Zhe Wang , Changsheng Peng","doi":"10.1016/j.jvcir.2025.104703","DOIUrl":"10.1016/j.jvcir.2025.104703","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) are extensively utilized in both military and civilian sectors, offering benefits and posing challenges. Traditional infrared small target detection techniques often suffer from high false alarm rates and low accuracy. To overcome these issues, we propose the Depthwise Separable Residual Dense Attention Network (DSRDANet), which redefines the detection task as a residual image prediction problem. This approach features an Adaptive Adjustment Segmentation Module (AASM) that uses depthwise separable residual dense blocks to extract detailed hierarchical features during encoding. Additionally, multi-scale feature fusion blocks are included to thoroughly aggregate multi-scale features and enhance residual image reconstruction during decoding. Furthermore, the Channel Attention Modulation Module (CAMM) is designed to model channel interdependencies and spatial encoding, optimizing the outputs from AASM by adjusting feature importance distribution across channels, ensuring comprehensive target attention. Experimental results on datasets for infrared small UAV target detection and tracking in various backgrounds validate our approach. Compared to state-of-the-art methods, our technique significantly enhances performance, improving the average F1 score by nearly 0.1, the IOU by 0.12, and the CG by 0.66.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"116 ","pages":"Article 104703"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

DFF-Matcher: Robust cross-source registration with density-fused feature and bidirectional consensus matching DFF-Matcher：具有密度融合特征和双向一致性匹配的鲁棒跨源配准

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-03-01 Epub Date: 2026-02-21 DOI: 10.1016/j.jvcir.2026.104746

Rong Guo , Zhenxuan Zeng , Jiang Wu , Xiyu Zhang , Siwen Quan , Zhongwen Hu , Yu Zhu , Jiaqi Yang

{"title":"DFF-Matcher: Robust cross-source registration with density-fused feature and bidirectional consensus matching","authors":"Rong Guo , Zhenxuan Zeng , Jiang Wu , Xiyu Zhang , Siwen Quan , Zhongwen Hu , Yu Zhu , Jiaqi Yang","doi":"10.1016/j.jvcir.2026.104746","DOIUrl":"10.1016/j.jvcir.2026.104746","url":null,"abstract":"<div><div>Cross-source point cloud registration plays a pivotal role in enabling seamless 3D perception across heterogeneous sensors. However, this task remains highly challenging due to significant density variations, sensor-specific noise, and partial overlaps between heterogeneous sensors. To address these challenges, we propose DFF-Matcher, a robust framework that integrates density-robust feature learning and bidirectional consensus matching to bridge domain gaps across different sensors. Our approach introduces a density-fused feature module to handle significant point density variations and a self-attention enhanced matching strategy to ensure reliable correspondence estimation. This unified framework establishes a new paradigm for cross-source registration, achieving superior performance across diverse sensor modalities. Extensive experiments demonstrate significant improvements, including 25.4% higher feature matching recall and 22.2% greater registration recall on challenging Kinect-LiDAR datasets, while maintaining robust performance in both indoor and outdoor scenarios.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"116 ","pages":"Article 104746"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147398302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

ShoeMatch3D: Attention-Enhanced deep learning framework for high-precision 3D shoeprint comparison ShoeMatch3D：用于高精度3D鞋印比较的注意力增强深度学习框架

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1016/j.jvcir.2026.104730

Binrui Li , Zhihan Tian , Linyu Huang , Yong Guo

引用次数: 0

Unified global–local feature modeling via reverse patch scaling for image manipulation localization 统一全局-局部特征建模，通过反向补丁缩放实现图像操作定位

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-03-01 Epub Date: 2026-01-23 DOI: 10.1016/j.jvcir.2026.104731

Jingying Cai , Hang Cheng , Jiabin Chen , Haichou Wang , Meiqing Wang

{"title":"Unified global–local feature modeling via reverse patch scaling for image manipulation localization","authors":"Jingying Cai , Hang Cheng , Jiabin Chen , Haichou Wang , Meiqing Wang","doi":"10.1016/j.jvcir.2026.104731","DOIUrl":"10.1016/j.jvcir.2026.104731","url":null,"abstract":"<div><div>Image manipulation localization requires comprehensive extraction and integration of global and local features. However, existing methods often adopt parallel architectures that process semantic context and local details separately, leading to limited interaction and fragmented representations. Moreover, applying uniform patching strategies across all layers ignores the varying semantic roles and spatial properties of deep features. To address these issues, we propose a unified framework that derives local representations directly from hierarchical global features. A reverse patch scaling strategy assigns smaller patch sizes and larger overlaps to deeper layers, enabling dense local modeling aligned with increasing semantic abstraction. An asymmetric cross-attention module improves feature interaction and consistency. Additionally, a dual-strategy decoder fuses multi-scale features via concatenation and addition, while a statistically guided edge awareness module models local variance and entropy from the predicted mask to refine boundary perception. Extensive experiments show that our method outperforms state-of-the-art approaches in both accuracy and robustness.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"116 ","pages":"Article 104731"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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