Asymmetric double networks mutual teaching for unsupervised person Re-identification

Unsupervised person re-identification (Re-ID) has always been challenging in computer vision. It has received much attention from researchers because it does not require any labeled information and can be freely deployed to new scenarios. Most unsupervised person Re-ID research studies produce and optimize pseudo-labels by iterative clustering algorithms on a single network. However, these methods are easily affected by noisy labels and feature variations caused by camera shifts, which will limit the optimization of pseudo-labels. In this paper, we propose an Asymmetric Double Networks Mutual Teaching (ADNMT) architecture that uses two asymmetric networks to generate pseudo-labels for each other by clustering, and the pseudo-labels are updated and optimized by alternate training. Specifically, ADNMT contains two asymmetric networks. One network is a multiple granularity network, which extracts pedestrian features of multiple granularity that correspond to numerous classifiers, and the other network is a conventional backbone network, which extracts pedestrian features that correspond to a classifier. Furthermore, because the camera style changes seriously affect the generalization ability of the proposed model, this paper designs Similarity Compensation of Inter-Camera (SCIC) and Similarity Suppression of Intra-Camera (SSIC) according to the camera ID of the pedestrian images to optimize the similarity measure. Extensive experiments on multiple Re-ID benchmark datasets show that our proposed method achieves superior performance compared with the state-of-the-art unsupervised person re-identification methods.