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 this https URL

本文提出了一种新颖的方法，即利用源和目标领域之间的桥梁来解决无监督领域自适应人员再识别（UDA re-ID）问题，该方法专注于显式地建模合适的中间领域以表征这种桥梁，并提出了中间域模块（IDM）来生成中间域表示，以及通过 bridge loss 和 diversity loss 约束中间域的生成。在各种常见的 UDA re-ID 任务中，该方法表现出比现有算法显著的性能优势，并且在挑战性的 MSMT17 基准测试中，mAP 的增益高达 7.7％。