DDAM-PS: Diligent Domain Adaptive Mixer for Person Search
Document Type
Conference Proceeding
Publication Title
Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
Abstract
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 normaware 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 https://github.com/mustansarfiaz/DDAM-PS.
First Page
6674
Last Page
6683
DOI
10.1109/WACV57701.2024.00655
Publication Date
1-1-2024
Keywords
Algorithms, Algorithms, Image recognition and understanding, Video recognition and understanding
Recommended Citation
M. Almansoori et al., "DDAM-PS: Diligent Domain Adaptive Mixer for Person Search," Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024, pp. 6674 - 6683, Jan 2024.
The definitive version is available at https://doi.org/10.1109/WACV57701.2024.00655