Cooperative Refinement Learning for domain adaptive person Re-identification

Abstract

Domain adaptive person re-identification (re-ID) targets at identifying the same persons’ images in unlabeled target domain. Some existing domain adaptive Person re-ID methods assigned pseudo labels by clustering algorithms on the target domain, which tends to generate noisy labels and neglect samples with low confidence as outliers. These may hinder the retraining process, thereby limiting the model’s generalization ability. In order to overcome these problems, we propose a Cooperative Refinement Learning (CooRL) framework, which resists noisy labels and takes advantage of the outliers by developing a multi-branches structure with refinement mechanism. Specifically, a mean-attention guided network is leveraged to learn more complementary features from pure and noisy samples generated by clustering, which includes a mean network and two sub-branches. Meanwhile, to better optimize the neural networks, CooRL jointly refines and aligns the pseudo labels of sub-branches by progressively adjusting the predicted logits through the mean network. Comprehensive experimental results have demonstrated that our proposed method can achieve excellent performances on benchmark datasets.