Diverse Feature Learning Network With Attention Suppression and Part Level Background Suppression for Person Re-Identification

Abstract

In this paper, we propose a Diverse Feature Learning Network with Attention Suppression and Part Level Background Suppression (DFLN) for person re-identification (ReID). DFLN includes two key components: attention suppression mechanism (ASM) and part level background suppression mechanism (PLBSM). Firstly, despite attention mechanism has made great progress in current state-of-the-art ReID methods, they can only pay attention to the most salient region but ignore other discriminative information limiting the diversity of networks, which is not optimal for ReID due to the models tend to match persons by diverse clues (e.g., legs, arms, body, logo of clothes). To tackle the limitation above, we propose the ASM to assist the network to make full use of the most salient features and capture the other sub-salient features, so as to attain diverse features to improve the network performance. Secondly, we adopt a novel PLBSM to develop the part-based method which is proved effective for enhancing the diversity of ReID network. The PLBSM consists of a part feature refined module and a background suppression loss function, and aims to attain pure part level feature by filtering background clutter. Our DFLN integrates the ASM and part-based method developed by PLBSM into an end-to-end network and is able to extract robust diversity feature representations leading to higher performance. Extensive experimental results demonstrate the effectiveness of each component and our method achieves state-of-the-art results on mainstream person re-identification datasets.