@article{DONG2026, 
author = {Neng DONG and Minghong XIE and Yafei ZHANG and Fan LI and Huafeng LI and Tingting TAN},
title = {Knowledge guidance and fine-grained information enhancement for unsupervised domain adaptation person re-identification},
year = {2026},
journal = {Journal of Chongqing University},
volume = {49},
number = {2},
pages = {81-91},
keywords = {person re-identification, unsupervised domain adaptation, knowledge guidance, fine-grained information enhancement},
url = {https://www.sciopen.com/article/10.11835/j.issn.1000-582X.2026.02.007},
doi = {10.11835/j.issn.1000-582X.2026.02.007},
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.}
}