  Person search (PS) is a challenging computer vision problem where the
objective is to achieve joint optimization for pedestrian detection and
re-identification (ReID). Although previous advancements have shown promising
performance in the field under fully and weakly supervised learning fashion,
there exists a major gap in investigating the domain adaptation ability of PS
models. In this paper, we propose a diligent domain adaptive mixer (DDAM) for
person search (DDAP-PS) framework that aims to bridge a gap to improve
knowledge transfer from the labeled source domain to the unlabeled target
domain. Specifically, we introduce a novel DDAM module that generates moderate
mixed-domain representations by combining source and target domain
representations. The proposed DDAM module encourages domain mixing to minimize
the distance between the two extreme domains, thereby enhancing the ReID task.
To achieve this, we introduce two bridge losses and a disparity loss. The
objective of the two bridge losses is to guide the moderate mixed-domain
representations to maintain an appropriate distance from both the source and
target domain representations. The disparity loss aims to prevent the moderate
mixed-domain representations from being biased towards either the source or
target domains, thereby avoiding overfitting. Furthermore, we address the
conflict between the two subtasks, localization and ReID, during domain
adaptation. To handle this cross-task conflict, we forcefully decouple the
norm-aware embedding, which aids in better learning of the moderate
mixed-domain representation. We conduct experiments to validate the
effectiveness of our proposed method. Our approach demonstrates favorable
performance on the challenging PRW and CUHK-SYSU datasets. Our source code is
publicly available at \url{https://github.com/mustansarfiaz/DDAM-PS}.
