Style Variable and Irrelevant Learning for Generalizable Person Re-identification

Abstract

Domain generalization person re-identification (DG-ReID) has gained much attention recently due to the poor performance of supervised re-identification on unseen domains. The goal of domain generalization is to develop a model that is insensitive to domain bias and can perform well across different domains. In this article, We conduct experiments to verify the importance of style factors in domain bias. Specifically, the experiments are to affirm that style bias across different domains significantly contributes to domain bias. Based on this observation, we propose style variable and irrelevant learning (SVIL) to eliminate the influence of style factors on the model. Specifically, we employ a style jitter module (SJM) that enhances the style diversity of a specific source domain and reduces the style differences among various source domains. This allows the model to focus on identity-relevant information and be robust to style changes. We also integrate the SJM module with a meta-learning algorithm to further enhance the model’s generalization ability. Notably, our SJM module is easy to implement and does not add any inference cost. Our extensive experiments demonstrate the effectiveness of our approach, which outperforms existing methods on DG-ReID benchmarks.