Curriculum Enhanced Supervised Attention Network for Person Re-Identification

Abstract

Though deep learning methods in Person Re-ID have increased its performance, the field is still confronted with great challenges such as pedestrian misalignment due to the inferior pedestrian detector. To solve such problems, we propose Curriculum Enhanced Supervised Attention Network (CE-SAN): Firstly, an attention module is trained under supervision, which helps the network further emphasize the key information and associate the local and global branch together, leading to a better exploitation of the discriminative features. Secondly, a curriculum design is adopted to divide the dataset into subsets according to the distribution density of the training samples, enabling the network to learn gradually to increase the capability. Moreover, The CE-SAN is easy to be plugged in most of the backbones with high generalization ability. To prove the CE-SAN's superiority, experiments are conducted on three datasets, CE-SAN achieves competitive performance with the state-of-the-arts on Market-1501 and DukeMTMC-reID. Particularly, on the most challenging MSMT17, it outperforms the state-of-the-art methods by 5.2 $\%$ in Rank-1 and 6.5 $\%$ in mAP.