Discrepant mutual learning fusion network for unsupervised domain adaptation on person re-identification

Existing methods mainly assign pseudo labels by clustering algorithms to solve the problems in domain adaptive pedestrian re-identification caused by unlabeled target domain data and scene style differences between the source and the target domains. But clustering algorithms with single network may generate incorrect noisy pseudo labels which affects the effectiveness of domain adaptation. To address this problem, the dual-branch teacher-student networks uses two networks with almost the same structures to assign soft pseudo labels. However, the soft pseudo labels assigned are largely the same because of the similar structure of the dual networks, which may lead to the same result with single-network training. Therefore, this paper proposes the discrepant mutual learning fusion network to improve the performance of unsupervised domain adaptive person re-identification by both increasing the difference between dual networks and enhancing their feature expressiveness. Firstly, this paper proposes the discrepant dual-branch network (DDNet) to mine the global and local features of the network by constructing two branches with different depths, and constructs the feature random scaling (FRS) module to further enhance the diversity of the extracted feature. Secondly, the feature fusion (FF) module is built to fuse the discrepant features generated by DDNet, and achieve mutual supervise learning of the fusion classification results, which enhances the ability of network feature expression. Experiments show that the proposed method outperforms most classical domain adaptive methods in recognition accuracy.