Multi-View Evolutionary Training for Unsupervised Domain Adaptive Person Re-Identification

Clustering-based approaches have been successfully applied to unsupervised domain adaptation (UDA) tasks for person re-identification (Re-ID), where no annotations are provided in target domain. However, the clustering process is sensitive to noises, leading to imperfect pseudo labels that could damage the training performance. In this work, we propose a Multi-view Evolutionary Training (MET) method to effectively reduce noises in clustering results from two dimensions. First, to improve the clustering accuracy at each time frame (i.e. snapshot quality), a Multi-view Diffusion (MvD) module is proposed. Through capturing data relationships from multiple viewpoints and aggregating their information, noises and bias from each individual viewpoint can be eliminated, and more reliable similarity matrix can be produced for clustering. Second, to improve the temporal consistency between clustering at different iterations, i.e. temporal consistency, we propose an Evolutionary Local Refinement (ELR) module, which utilizes the previous clustering results to guide and improve current results, and further make the training process more stable and robust. Extensive experiments demonstrate that our method can provide clustering results with high quality, and achieve state-of-the-art performance on UDA Re-ID.