重庆大学学报2026，Vol.49Issue(2)：81-91,11.DOI:10.11835/j.issn.1000-582X.2026.02.007
知识引导和细粒度信息增强的无监督域自适应行人再识别
Knowledge guidance and fine-grained information enhancement for unsupervised domain adaptation person re-identification
摘要
Abstract
Unsupervised domain adaptation(UDA)aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain,playing a very important role in person re-identification.In real-world applications,video-based pedestrian data are often available,making it feasible to obtain single-camera-view labels in the target domain.However,existing UDA methods typically ignore this readily accessible information,thereby limiting performance improvements.To address this issue,we propose a knowledge-guided and fine-grained information enhancement framework for UDA person re-identification.A novel paradigm is introudced that leverages single-view labeled pedestrian samples in the target domain to fully exploit intra-domain information.Meanwhile,source-domain knowledge is used as guidance to assist the model to extract more discriminative target-domain pedestrian representations,effectively mitigating domain shift compared with conventional knowledge-transfer strategies.Furthermore,local pedestrian cues are integrated into global features to strengthen fine-grained feature expression.Experiments conducted on two publicly datasets fully demonstrate the effectiveness and superiority of the proposed method.关键词
行人再识别/无监督域自适应/知识引导/细粒度信息增强Key words
person re-identification/unsupervised domain adaptation/knowledge guidance/fine-grained information enhancement分类
信息技术与安全科学引用本文复制引用
董能,谢明鸿,张亚飞,李凡,李华锋,谭婷婷..知识引导和细粒度信息增强的无监督域自适应行人再识别[J].重庆大学学报,2026,49(2):81-91,11.基金项目
国家自然科学基金(61966021,61562053) （61966021,61562053）
大学生创新创业训练计划项目(202010674098). Supported by National Natural Science Foundation of China(61966021,61562053)and College Students'Innovative Entrepreneurial Training Plan Program(202010674098). （202010674098）
