Cross-domain Person Re-identification on Adaptive Fusion Network

Unsupervised cross-domain person re-identification aims to transfer the knowledge learned from labeled source domain to unlabeled target domain, which has attracted wide attention due to its practicability and effectiveness. Cross-domain person re-identification based on clustering can generate pseudo-labels and optimize the model to make its performance better than other methods. However, these methods rely too much on the accuracy of clustering pseudo labels and ignore to deal with pseudo-label noise, which leads to the continuous expansion of noise with network iteration and affects the robustness of the models. To address this problem, this paper proposes a method based on fine-grained style transfer and adaptive fusion network, which uses dual network structure to learn together and fuse the learned knowledge to obtain a fusion network. To treat the learning ability of the two networks differently, an adaptive fusion strategy is designed based on the different weights of the two networks in each fusion process. At the same time, a fine-grained style transfer module is used to process the target domain dataset, thereby reducing the sensitivity of person images to camera transformation. On the person re-identification benchmark datasets Market1501, DukeMTMC-ReID and MSMT17, the effectiveness of the proposed method was verified by comparing mean average precision and Rank-n with the state-of-the-art methods. © 2022 Science Press. All rights reserved.