Cross-Attention Guided Local Feature Enhanced Multi-Branch Network for Person Re-Identification

Abstract

The purpose of person re-identification is to identify and retrieve the same individual across different scenes, angles, and times. As an instance-level recognition problem, human re-identification relies on discriminative features, but is not determined by a single salient feature. This requires us to extract identity-specific features from multiple perspectives. In this paper, we propose a cross-attention guided local feature enhanced multi-branch network, which includes a global branch, a partial branch, and an attention channel branch. The network is guided by attention and jointly extracts local and global features to capture discriminative identity features from multiple aspects. To enable each branch to effectively mine identity information, we design modules dedicated to the function of each branch, making the branch network capture discriminative features. Finally, we conducted extensive testing on the Market1501 and CUHK03 datasets and achieved outstanding results.