  Person search is a challenging task which aims to achieve joint pedestrian
detection and person re-identification (ReID). Previous works have made
significant advances under fully and weakly supervised settings. However,
existing methods ignore the generalization ability of the person search models.
In this paper, we take a further step and present Domain Adaptive Person Search
(DAPS), which aims to generalize the model from a labeled source domain to the
unlabeled target domain. Two major challenges arises under this new setting:
one is how to simultaneously solve the domain misalignment issue for both
detection and Re-ID tasks, and the other is how to train the ReID subtask
without reliable detection results on the target domain. To address these
challenges, we propose a strong baseline framework with two dedicated designs.
1) We design a domain alignment module including image-level and task-sensitive
instance-level alignments, to minimize the domain discrepancy. 2) We take full
advantage of the unlabeled data with a dynamic clustering strategy, and employ
pseudo bounding boxes to support ReID and detection training on the target
domain. With the above designs, our framework achieves 34.7% in mAP and 80.6%
in top-1 on PRW dataset, surpassing the direct transferring baseline by a large
margin. Surprisingly, the performance of our unsupervised DAPS model even
surpasses some of the fully and weakly supervised methods. The code is
available at https://github.com/caposerenity/DAPS.
