通信与信息技术Issue(2)：17-22,6.
融合GAT与Transformer的行人重识别方法
Person re-identification method combining GAT and Transformer
摘要
Abstract
In the person re-identification task,the convergence degree of the same type of person in the feature space is not good,and the recognition accuracy of the system is not high.In order to solve the problem,a person re-identification method(Fusion of graph neural networks with the Transformer,FGT)combining Graph Attention Network(GAT)and Transformer is proposed.By combining the global information processing ability of Transformer with the improved local feature extraction ability of GAT,it brings deeper information understanding and wider feature expression ability to the model.The improved GAT was used to analyze the relationship and structural features between input images to enhance the processing of structured data.The Depthwise Separable Convolution Parallel Denoising(DCD)module is improved to reduce the interference of noise on feature extraction.Deep Supervision(DS)is introduced to avoid the gra-dient disappearance problem and promote the rapid convergence of the network.The experimental results on the Market-1501,DukeMT-MC-ReID,CUHK03,and MSMT17 datasets demonstrate that this model effectively enhances the degree of person feature aggregation and improves the accuracy of re-identification.关键词
行人重识别/图注意力神经网络/Transformer/深度可分离卷积并行去噪/深度监督Key words
Person re-identification/Graph attention network/Transformer/Depthwise separable convolution parallel denoising/Deep supervision分类
计算机与自动化引用本文复制引用
庄爽,宋建辉,刘鑫..融合GAT与Transformer的行人重识别方法[J].通信与信息技术,2025,(2):17-22,6.基金项目
辽宁省教育厅高等学校基本科研项目(项目编号:LJKZ0275)辽宁省属本科高校基本科研业务费专项资金资助 沈阳市中青年科技创新人才支持计划项目(项目编号:RC210247) （项目编号:LJKZ0275）
