Unsupervised confident co-promoting: refinery for pseudo labels on unsupervised person re-identification

Hao Tang; Kun Zhan

doi:10.1117/12.2640850

13 October 2022 Unsupervised confident co-promoting: refinery for pseudo labels on unsupervised person re-identification

Hao Tang, Kun Zhan

Author Affiliations +

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122870P (2022) https://doi.org/10.1117/12.2640850
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Unsupervised domain adaptation (UDA) person re-identification (re-ID) aims to apply useful knowledge learned on labeled source domain datasets to unlabeled target domain datasets. Most successful UDA re-ID methods combine clustering to generate pseudo labels for feature learning and further fine-tuning on the target domain in an alternating manner. However, the interaction of the two steps is offline, which may make the noisy pseudo labels greatly hinder the classification and retrieval ability of the whole model. In order to purify these noisy pseudo labels, in this paper, a framework called Unsupervised Confident Co-promoting (UCC) is proposed. Specially, two peer teach-student co-training models are adopted simultaneously to online refine the noisy pseudo labels with the offline clustering algorithm and supervise each other during iterations. More significantly, we introduce a confidence strategy that greatly improves the confidence of generating pseudo labels in the case of multi-network collaboratively guided learning. The combination of the above two allows our final method to greatly improve noisy pseudo labels on re-ID task, achieve a huge performance boost and generalize to more deep learning domains. Moreover, our method achieves a significant improvement in common evaluation indicators in the four most common re-ID experiments compared to the state-of-the-art (SOTA) methods, and even on some results is comparable to the supervised learning method.

Citation Download Citation

Hao Tang and Kun Zhan "Unsupervised confident co-promoting: refinery for pseudo labels on unsupervised person re-identification", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122870P (13 October 2022); https://doi.org/10.1117/12.2640850

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Chromium

Data modeling

Performance modeling

Cameras

Detection and tracking algorithms

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Machine learning

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