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Cluster-Based Distribution Alignment For Generalizable Person Re-Identification

Person re-identification (ReID) aims to identify a person of interest across surveillance cameras. Although supervised ReID methods have made a great success, most of them suffer a large drop in performance on unseen domains. The key idea to solve this problem lies in the distribution alignment of pedestrian images. In this paper, we design a generalizable person ReID framework which is trained on the source domain but still generalizes well on unseen domains. To achieve this goal, our framework adaptively searches and aligns the underlying distributions among images through cluster-based Batch Normalization (BatchNorm). Specifically, the framework iteratively clusters images based on the style features extracted from middle layers of the networks. The clustering labels are then utilized to guide the cluster-based BatchNorm, which standardizes features in the same cluster within a batch. Extensive experiments on several ReID benchmarks demonstrate the effectiveness of our approach.