Global-local transformer for unsupervised person re-identification

Meng-Si Xie; Si-Bao Chen

doi:10.1117/12.2684980

19 October 2023 Global-local transformer for unsupervised person re-identification

Meng-Si Xie, Si-Bao Chen

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127090Y (2023) https://doi.org/10.1117/12.2684980
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Unsupervised person re-identification (Re-ID) focuses on studying the issues of person Re-ID in the open world where there are no available annotated labels for pedestrian images. The original transformer only learns global features. It ignores local features and fails to consider high-level local information on images. Therefore, we introduce a transformer-based multi-branch unsupervised person Re-ID framework, named GLTReID, with learning more robust features of pedestrian images. Specifically, we split the original transformer into one global branch and two local branches, with learning global and local features in parallel. The global branch learns global features of pedestrian images, and two local branches learn local features of different parts of pedestrian images. Numerous experiments are conducted on several person Re-ID datasets, including CUHK03, Market-1501 and MSMT17. Numerous experiments denote that our method is better than most unsupervised learning methods and unsupervised domain adaptation methods in person Re-ID.

Citation Download Citation

Meng-Si Xie and Si-Bao Chen "Global-local transformer for unsupervised person re-identification", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127090Y (19 October 2023); https://doi.org/10.1117/12.2684980

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