Dual branch guided contrastive learning for unsupervised pedestrian re-identification

The current unsupervised pedestrian re-identification algorithms using residual networks can only extract rough global features, but it can’t adequately reflect subtle local features. In addition, the pseudo labels generated by clustering methods introduce noise, which will affect the performance of feature discrimination. A dual branch guided contrastive learning method was proposed. Firstly, an effective feature extraction method was introduced, which divided the extracted features into global branches and local branches to improve the utilization of local information. Secondly, the consistency between global and local features was proposed to refine the pseudo labels for global feature prediction, utilizing the complementary relationship between local and global features, thereby effectively reducing the noise generated by pseudo label clustering. Finally, a contrastive learning module was proposed to perform contrastive learning on refined labels and improve the robustness of the model. The experimental results on the Market1501, DukeMTMC-ReID, and MSMT17 datasets validate the effectiveness of the proposed method.