Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-Identification

Yi Zheng, Shixiang Tang, Guolong Teng, Yixiao Ge, Kaijian Liu, Jing Qin, Donglian Qi, Dapeng Chen; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8371-8381

Adaptive person re-identification (adaptive ReID) targets at transferring learned knowledge from the labeled source domain to the unlabeled target domain. Pseudo-label-based methods that alternatively generate pseudo labels and optimize the training model have demonstrated great effectiveness in this field. However, the generated pseudo labels are inaccurate and cannot reflect the true semantic meaning of the unlabeled samples. We consider such inaccuracy stems from both the lagged update of the pseudo labels as well as the simple criterion of the employed clustering method. To tackle the problem, we propose an online pseudo label generation by hierarchical cluster dynamics for adaptive ReID. In particular, hierarchical label banks are constructed for all the samples in the dataset, and we update the pseudo labels of the sample in each coming mini-batch, performing the model optimization and the label generation simultaneously. A new hierarchical cluster dynamics is built for the label update, where cluster merge and cluster split are driven by a possibility computed by the label propagation. Our method can achieve better pseudo labels and higher reid accuracy. Extensive experiments on Market-to-Duke, Duke-to-Market, MSMT-to-Market, MSMT-to-Duke, Market-to-MSMT, and Duke-to-MSMT verify the effectiveness of our proposed method.