Journal of Visual Communication and Image Representation最新文献

LWU-YOLO: A lightweight algorithm for small object detection in UAV applications LWU-YOLO：一种用于无人机小目标检测的轻量级算法

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-04-01 Epub Date: 2026-03-28 DOI: 10.1016/j.jvcir.2026.104791

Yapeng Li , Ting Wang , Tao Li , Xin Yang

引用次数: 0

GGCN: Gait Recognition with Generate Network and Convolutional Neural Network 基于生成网络和卷积神经网络的步态识别

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.2026.104790

Hao Qin , Zhenxue Chen , Qingqiang Guo , Q.M. Jonathan Wu , Mengxu Lu

引用次数: 0

Beyond bounding boxes: Segmentation supervision for robust object detection in fisheye images 超越边界框：鱼眼图像中鲁棒目标检测的分割监督

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-04-01 Epub Date: 2026-04-04 DOI: 10.1016/j.jvcir.2026.104798

Arda Oztuner, Mehmet Kilicarslan

{"title":"Beyond bounding boxes: Segmentation supervision for robust object detection in fisheye images","authors":"Arda Oztuner, Mehmet Kilicarslan","doi":"10.1016/j.jvcir.2026.104798","DOIUrl":"10.1016/j.jvcir.2026.104798","url":null,"abstract":"<div><div>Fisheye cameras pose significant object detection challenges due to severe radial distortion, rendering traditional axis-aligned bounding boxes suboptimal for warped object shapes. We propose a pipeline that transforms bounding box annotations into instance segmentation masks using the Segment Anything Model (SAM) and validate mask fidelity against expert ground truth in both rectilinear and distorted domains. We benchmark various models on the Fisheye8K dataset, demonstrating the architectural generalizability of our approach across YOLOv8, YOLOv11, and YOLOv12. Results show that segmentation-based supervision yields substantial performance gains, improving the mean average precision (mAP@[0.5:0.95]) by up to 10 absolute points over models trained with bounding boxes, and up to 12 points in distorted outer regions. Furthermore, our approach outperforms state-of-the-art methods and establishes a new benchmark for fisheye object detection. This work highlights the specific theoretical and empirical benefits of automated segmentation-based annotation within complex, distorted imaging domains.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"117 ","pages":"Article 104798"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147656944","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

Multi-view recursive gated convolutions for 3D object recognition and retrieval 三维目标识别与检索的多视图递归门控卷积

IF 3.1 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2026-04-01 Epub Date: 2026-03-26 DOI: 10.1016/j.jvcir.2026.104792

Jiangzhong Cao, Yue Cai, Huan Zhang

{"title":"Multi-view recursive gated convolutions for 3D object recognition and retrieval","authors":"Jiangzhong Cao, Yue Cai, Huan Zhang","doi":"10.1016/j.jvcir.2026.104792","DOIUrl":"10.1016/j.jvcir.2026.104792","url":null,"abstract":"<div><div>Multi-view-based 3D shape recognition methods perform 3D object recognition and retrieval by processing series of images from various angles to generate a compact 3D descriptor. However, existing approaches often focus on integrating information from multiple views without addressing spatial interactions and redundancy when similar views are used. To overcome these challenges, we propose a novel framework, Multi-view Recursive Gated Convolutions (MVRGC). Our method first extracts features from multiple views at different scales, allowing for initial interaction of information across these views. Recursive gated convolutions are then applied to capture deeper spatial reciprocity and fine-tune feature interactions among views. Additionally, a preferred view module is introduced to reduce view redundancy by favoring distinctive and representative views. This module selects a subset of views that best describe the object while minimizing overlap. Experimental results on shape benchmark datasets demonstrate that MVRGC outperforms existing methods in 3D object recognition and retrieval tasks.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"117 ","pages":"Article 104792"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147600946","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

Densely aggregated U-net with spatial-spectral interaction transformer for hyperspectral compressed imaging reconstruction 基于空间-光谱相互作用转换器的密集聚集u网用于高光谱压缩成像重建

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.2026.104795

Yun-Hui Li

{"title":"Densely aggregated U-net with spatial-spectral interaction transformer for hyperspectral compressed imaging reconstruction","authors":"Yun-Hui Li","doi":"10.1016/j.jvcir.2026.104795","DOIUrl":"10.1016/j.jvcir.2026.104795","url":null,"abstract":"<div><div>Hyperspectral imaging offers critical spectral information for applications such as material analysis and camouflage recognition. However, the acquisition of hyperspectral data cubes is inherently constrained by the Nyquist sampling theorem. While compressed sensing theory enables snapshot imaging by compressing the data cube into a 2D measurement, the ill-posed reconstruction remains a significant challenge. Recent deep learning methods, particularly vision transformers, have advanced the state-of-the-art (SOTA). Despite this, existing networks typically employ spectral or spatial self-attentions in isolation, blindly pursuing a global receptive field at the cost of computational efficiency and representational flexibility. Additionally, the vanilla skip connection in U-Nets is insufficient for effective multi-scale information transmission between encoder and decoder. To address these issues, we propose a Densely aggregated U-Net with a Spatial-Spectral Interaction Transformer (DSST). DSST parallelizes patch-based spectral self-attention and window-based spatial self-attention, complemented by an interaction mechanism. Furthermore, it introduces a densely aggregated skip connection to collect multi-scale features and bridge the semantic gap. Experimental results on both simulated and real-world scenes demonstrate that DSST achieves competitive performance with lower computational and memory costs compared to other end-to-end networks. Moreover, it offers faster inference speeds than deep unfolding networks.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"117 ","pages":"Article 104795"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147600948","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

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

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

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

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