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}.

翻译：行人搜索（PS）是一个具有挑战性的计算机视觉问题，其目标是在行人检测与行人重识别（ReID）中实现联合优化。尽管先前在全监督和弱监督学习范式下取得了显著进展，但针对行人搜索模型的领域自适应能力研究仍存在明显空白。本文提出了一种用于行人搜索的勤勉领域自适应混合器（DDAM）框架（DDAM-PS），旨在弥合标注源域与未标注目标域之间的知识迁移鸿沟。具体而言，我们引入了一种新颖的DDAM模块，通过融合源域与目标域表征生成适度的混合域表征。该DDAM模块通过鼓励域混合来最小化两个极端域之间的差异，从而增强ReID任务性能。为实现这一目标，我们提出了两个桥接损失函数和一个差异损失函数。桥接损失函数旨在引导适度混合域表征与源域、目标域表征保持合理距离；差异损失函数则用于防止适度混合域表征向源域或目标域倾斜，避免过拟合。此外，我们解决了领域自适应过程中定位与ReID两个子任务的冲突问题。为处理这种跨任务冲突，我们强制解耦了范数感知嵌入，这有助于更好地学习适度混合域表征。实验验证了所提方法的有效性，在具有挑战性的PRW和CUHK-SYSU数据集上取得了优异性能。我们的源代码已公开于\url{https://github.com/mustansarfiaz/DDAM-PS}。