Unsupervised Person Re-Identification Based on Intermediate Domains

Unsupervised domain adaptive person re-identification (UDA re-ID) aims to transfer knowledge learned from the labeled source domain to the unlabeled target domain. Most recent methods focus on narrowing down the domain gap between the source and target domains while ignore the bridge between them. In this work, we explicitly model appropriate intermediate domains and construct two adaptation pairs ("source-intermediate" and "intermediate-target") instead of the original "source-target" one pair adaptation. The purpose is to ease the adaptation difficulties caused by large domain gaps, making the adaptation process more smooth. To generate the intermediate domain, we use image-to-image translation methods which generate images that have the same contents and ID labels shared with the source domain and similar style to the target domain. When evaluated on standard benchmarks, our proposed methods outperforms the state of the arts by a large margin on the target domains where mAP of our method is higher than IDM [12] by 1.4% and 2.2% when testing on Market-1501 and MSMT17 respectively.