Mutual Attention Learning Network for Domain Adaptive Person Re-identification. Issue 1 (April 2021)

• Record Type: Journal Article
• Authors: Zhu, Liming; Xiao, Junsheng
• Is Part Of: Journal of physics. Volume 1873:Issue 1(2021)
• Publication Date: 2021-04
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1873/1/012044
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Owing to its important role in video surveillance, person re-identification(re-ID) has become a hot research field of computer vision. The purpose of person re-ID is to identify the same person under different cameras. Domain adaptive person re-ID is to transfer the training model from labeled dataset to unlabeled dataset. Due to the neglect of key channels and key regions, the performance of most existing domain adaptive person re-ID methods is limited. To solve the above problem, this paper proposed a mutual attention learning network model (MALNet). It is based on channel attention and spatial attention mechanism. It consists of two channel-spatial attention convolutional neural networks (channel-spatial attention CNNs). Two networks are pre-trained in a source domain. They adapt to the target domain by the mutual attention learning strategy afterward. They distill and supervise each other to enhance the complementarity of MAL-Net. Moreover, a channel-spatial attention loss (CSAL) function is proposed to constrain and approximate the classification prediction distributions and feature triplet distributions. Extensive comparative experiments are carried out on two large-scale re-ID datasets (Market-1501 and DukeMTMC-reID). The performance of MAL-Net is better than other state-of-the-art methods, which proves the superiority and effectiveness of our method.