Multi-label cooperative learning for cross domain person re-identification

Cross-domain was an important application scenario in person re-identification, but the apparent difference of person image in illumination condition, shooting angle, imaging background and style between the source domain and target domain was the most important factor that leads to the decline of the generalization ability of person re-identification model. A cross-domain person re-identification method was proposed based on multi-label cooperative learning to solve the problem. Firstly, the semantic parsing model was used to construct the multi-label data based on semantic alignment, which was able to guide us to construct global features that pay more attention to the person area, achieve the purpose of semantic alignment, and reduce the background influence on cross-domain person re-identification. Furthermore, the collaborative learning average model was used to generate a multi-label representation of the person re-identification model based on global and local features after semantic alignment, reducing the interference of noisy hard labels in the cross-domain scenario. Finally, the semantic alignment model of multi-label based on a collaborative learning network framework was combined to improve the identification ability of re-identification model. The experiment results show that on the Market-1501 → DukeMTMC-relD, DukeMTMC-relD → Market-1501, Market-1501 → MSMT17, DukeMTMC-relD→MSMT17 cross-domain person re-identification data set, compared with the current state-of-the-artscross-domain person re-identification method NRMT, the mean average precision of this method is increased by 8. 3%, 8. 9%, 7. 6% and 7. 9%, respectively. Multi-label cooperative learning method has obvious advantages. © 2022 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.