DOMAIN GENERALIZATION METHOD FOR PERSON RE-ID USING METABIN AND MIXSTYLE

Recently, person re-identification with domain generalization has focused on reducing the gap between source domains and unseen target domains. Meta Batch Instance Normalization (MetaBIN) is one of the most effective methods for addressing the domain gap problem. However, the lack of style variation from limited source domains still makes diversifying virtual simulations difficult. To alleviate this, an improved generalizable person re-identification method is proposed; when this method is combined with MixStyle and meta-leaning, it is called MetaMix. Initially, we represent the MixStyle layers of MetaBIN to create a diverse style during the meta-learning process. Moreover, fine-tuning a BIN module is shown to be applicable for pre-trained re-identification (ReID) models by batch normalization. Extensive experimental results show the effectiveness of the proposed method in a large-scale cross-domain scenario.