Weighted Supervised Contrastive Learning and Domain Mixture for Generalized Person Re-Identification

Abstract

Domain generalization(DG) in person re-identification (ReID) attracts increasing attention due to its practical applications. It aims to learn a model that, after training on multiple source domains, can be applied directly to the unseen domains without further training. In order to develop a domain-robust model for unseen domains, we propose the Memory-based Anti-Hard-instances Domain-Mix(MAD) framework. Specifically, a memory-based non-parametric contrastive loss is adopted to replace the traditional parametric cross-entropy loss. To prevent overfitting on the source domains, we present an Anti-Hard-instances module to mitigate the effect of hard instances. We also introduce a Domain-Mix module to diversify the features in the source domains, further enhancing the generalization ability of our model. Extensive experiments on four large-scale ReID datasets fully demonstrate the strong generalization and competitiveness of our framework.