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Unsupervised person re-identification via multi-domain joint learning.
- Source :
-
Pattern Recognition . Jun2023, Vol. 138, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- • 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]
- Subjects :
- *DATA augmentation
*DEEP learning
*SOURCE code
*LEARNING
Subjects
Details
- Language :
- English
- ISSN :
- 00313203
- Volume :
- 138
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition
- Publication Type :
- Academic Journal
- Accession number :
- 162256830
- Full Text :
- https://doi.org/10.1016/j.patcog.2023.109369