DiffPS: Leveraging Prior Knowledge of Diffusion Model for Person Search

Person search aims to jointly perform person detection and re-identification by localizing and identifying a query person within a gallery of uncropped scene images. Existing methods predominantly utilize ImageNet pre-trained backbones, which may be less effective at capturing the contextual and fine-grained features crucial for person search. Moreover, they rely on a shared backbone feature for both person detection and re-identification, leading to suboptimal features due to conflicting optimization objectives. Recently, diffusion models have emerged as powerful vision backbones, capturing rich visual priors from large-scale datasets. In this paper, we propose DiffPS (Diffusion Prior Knowledge for Person Search), a novel framework that leverages a frozen pre-trained diffusion model while eliminating the optimization conflict between two sub-tasks. We analyze key properties of diffusion priors and propose three specialized modules: (i) Diffusion-Guided Region Proposal Network (DGRPN) for enhanced person localization, (ii) Multi-Scale Frequency Refinement Network (MSFRN) to mitigate shape bias, and (iii) Semantic-Adaptive Feature Aggregation Network (SFAN) to leverage text-aligned diffusion features. DiffPS sets a new state-of-the-art on CUHK-SYSU and PRW. Our code will be available online at the time of the publication.