Unsupervised person re-identification via multi-domain joint learning.

• Render the transferred model more generalizable by enhancing appearance diversity. • Achieve feature decoupling with the aid of data augmentation and a multi-label assignment strategy. • Improve the reliability of pseudo labels by exploiting the correlation of multiple clustering results. Deep learning techniques have achieved impressive progress in the task of person re-identification. However, how to generalize a learned model from the source domain to the target domain remains a long-standing challenge. Inspired by the fact that the enrichment of data diversity and the utilization of miscellaneous semantic features can lead to better generalization ability, we design a model that integrates a novel data augmentation method with a multi-label assignment strategy to achieve semantic features decoupling in the source domain. The pre-trained model is employed to extract several kinds of semantic features from the target dataset, and each kind of semantic features is regarded as a specific domain. We then cluster features of each domain and exploit the connection between different clustering results to perform self-distillation for generating more reliable pseudo labels. Finally, the obtained pseudo labels are used to fine-tune the pre-trained model to achieve model transfer from the source domain to the target one. Extensive experiments demonstrate that our approach outperforms some state-of-the-art methods by a clear margin and even surpass some supervised methods. Source code is available at: https://www.github.com/flychen321/MDJL. [ABSTRACT FROM AUTHOR]