  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.
