Multi-Domain Synchronous Refinement Network for Unsupervised Cross-Domain Person Re-Identification
Sikai Bai, Junyu Gao, Qi Wang, Xuelong Li · 2021
Unsupervised cross-domain person re-identification (re-ID) is a challenging task, because it is an open-set problem with completely unknown person identities in the target domain. Existing methods attempt to tackle the challenge by transferring image style across domains or generating pseudo labels in the target domain, whereas the valuable information in multiple domains (i.e., source domain, style-transferred data, and target domain) is not taken fully into consideration. To this end, we propose a novel multi-domain synchronous refinement (MDSR) network, where valuable knowledge from multiple domains is sufficiently exploited and refined to enforce the discriminative ability of the model. MDSR network contains two complementary modules dedicated to source-to-target domain adaptation and style-transferred data to the target domain adaptation, respectively. The domain adaptive knowledge from two modules is aggregated in the final stage. Extensive experiments verify our method achieves significant improvements over the state-of-the-art approaches on multiple unsupervised domain adaptative person re-ID tasks.