  Unsupervised domain adaptive person re-identification (UDA re-ID) aims at
transferring the labeled source domain's knowledge to improve the model's
discriminability on the unlabeled target domain. From a novel perspective, we
argue that the bridging between the source and target domains can be utilized
to tackle the UDA re-ID task, and we focus on explicitly modeling appropriate
intermediate domains to characterize this bridging. Specifically, we propose an
Intermediate Domain Module (IDM) to generate intermediate domains'
representations on-the-fly by mixing the source and target domains' hidden
representations using two domain factors. Based on the "shortest geodesic path"
definition, i.e., the intermediate domains along the shortest geodesic path
between the two extreme domains can play a better bridging role, we propose two
properties that these intermediate domains should satisfy. To ensure these two
properties to better characterize appropriate intermediate domains, we enforce
the bridge losses on intermediate domains' prediction space and feature space,
and enforce a diversity loss on the two domain factors. The bridge losses aim
at guiding the distribution of appropriate intermediate domains to keep the
right distance to the source and target domains. The diversity loss serves as a
regularization to prevent the generated intermediate domains from being
over-fitting to either of the source and target domains. Our proposed method
outperforms the state-of-the-arts by a large margin in all the common UDA re-ID
tasks, and the mAP gain is up to 7.7% on the challenging MSMT17 benchmark. Code
is available at https://github.com/SikaStar/IDM.
