This paper presents a novel person re-identification model, named Multi-Head Self-Attention Network (MHSA-Net), to prune unimportant information and capture key local information from person images. MHSA-Net contains two main novel components: Multi-Head Self-Attention Branch (MHSAB) and Attention Competition Mechanism (ACM). The MHSAB adaptively captures key local person information, and then produces effective diversity embeddings of an image for the person matching. The ACM further helps filter out attention noise and non-key information. Through extensive ablation studies, we verified that the Multi-Head Self-Attention Branch (MHSAB) and Attention Competition Mechanism (ACM) both contribute to the performance improvement of the MHSA-Net. Our MHSA-Net achieves competitive performance in the standard and occluded person Re-ID tasks.
@article{arxiv.2008.04015,
title = {MHSA-Net: Multi-Head Self-Attention Network for Occluded Person Re-Identification},
author = {Hongchen Tan and Xiuping Liu and Baocai Yin and Xin Li},
journal= {arXiv preprint arXiv:2008.04015},
year = {2022}
}