Adaptive Memorization With Group Labels for Unsupervised Person Re-Identification

Re-identification (re-ID) aims to identify a person's images across different cameras. However, the domain differences between different datasets make it a challenge for re-ID models trained on one dataset to be adapted to another. A variety of unsupervised domain adaptation methods tend to transfer learned knowledge from one domain to another by optimizing with pseudo-labels. Though impressive performances have been achieved, there are still some limitations. To be specific, these methods always generate one pseudo label for each unlabeled sample, which is hard to describe a person accurately and introduces a large number of noisy labels by one-shot clustering, thus hindering the retraining process and limiting generalization. To build more comprehensive descriptions of samples and mitigate the effects of noisy pseudo labels, this paper proposes an Adaptive Memorization with Group labels (AdaMG) framework for unsupervised person re-ID, which resists noisy labels and exploits the diversity of samples by developing a multi-branch structure with the adaptive memorization. In particular, group labels are generated for one sample in the unseen domain to learn more complementary and diverse features through clustering. Meanwhile, to better optimize the neural networks with noisy data, multiple memory structures are designed in AdaMG, which are updated adaptively according to the confidence of samples. Comprehensive experimental results have demonstrated that our proposed method can achieve excellent performances on benchmark datasets.