ICMiF: Interactive cascade microformers for cross-domain person re-identification

Abstract

Person re-identification (ReID) arises in many applications, such as video surveillance and intelligent security. A challenge of cross-domain person ReID is the notorious distribution drift problem. Improving the accuracy of pseudo labels can promote the model to fit the target domain, so it is crucial to extract discriminative person features. The transformer can extract discriminative person features due to its ability to capture long distance dependencies, but its poor generalization ability may aggravate the influence of noise pseudo labels. The isolation between local information modeling and global information modeling further prevents the extraction of the discriminative person features. Therefore, this paper proposes an interactive cascade transformer framework. Firstly, a lightweight transformer structure (microformer) with better generalization is proposed. It maintains the advantages of traditional transformers on extracting discriminative features, and avoids the aggravation of the influence of noise labels. Secondly, an interactive cascade microformer framework (ICMiF) is proposed, which promotes the interaction between local information modeling and global information modeling to enhance the person feature representation. Local information facilitates mining global attribute dependencies, and global information improves the accuracy of local information. The experimental results demonstrate that the proposed ICMiF outperforms state-of-the-art methods for the cross-domain person ReID tasks.