郑州大学学报（理学版）2025，Vol.57Issue(6)：51-57,7.DOI:10.13705/j.issn.1671-6841.2024031
基于双分支注意力特征融合的跨域行人重识别
Cross Domain Person Re-identification Based on Dual Branch Attention Feature Fusion
摘要
Abstract
With unsupervised cross domain pedestrian re-identification technology,labeled information could be transfered from the source domain to the target domain to cope with unlabeled situations,cluste-ring methods were to achieve unsupervised domain adaptation,so that to achieve cross domain pedestrian re-identification.However,clustering based on solely global features was susceptible to noise generated by inter domain differences,and single network structure training could lead to error amplification and af-fect model performance.A dual-branch attention-based fusion algorithm was proposed to extract&fuse invariant and specific features to enhance target domain generalization and to reduce clustering noise.At the same time,a symmetric network architecture was introduced for synchronous collaborative training,form a mutually supervised learning mechanism to effectively suppress overfitting problems.Experiments showed that on the Market-1501 and DukeMTMC-ReID datasets,the algorithm significantly improved the mAP and Rank accuracy of unsupervised cross domain pedestrian re-identification.关键词
行人重识别/无监督域适应/注意力机制/特征融合Key words
person re-identification/unsupervised domain adaptation/attention mechanism/feature fu-sion分类
信息技术与安全科学引用本文复制引用
马建红,靳岩,王亚辉,谷保平..基于双分支注意力特征融合的跨域行人重识别[J].郑州大学学报（理学版）,2025,57(6):51-57,7.基金项目
国家重点研发计划项目(2020YFB171240) （2020YFB171240）
郑州市协同创新重大专项(20XTZX06013) （20XTZX06013）
