Leveraging Prior Knowledge of Diffusion Model for Person Search

• URL: http://arxiv.org/abs/2510.01841v1
• Date: Thu, 02 Oct 2025 09:36:26 GMT
• Title: Leveraging Prior Knowledge of Diffusion Model for Person Search
• Authors: Giyeol Kim, Sooyoung Yang, Jihyong Oh, Myungjoo Kang, Chanho Eom,
• Abstract summary: 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.<n>Existing methods predominantly utilize ImageNet pre-trained backbones, which may be suboptimal for capturing the complex spatial context.<n>We propose DiffPS, a novel framework that leverages a pre-trained diffusion model while eliminating the optimization conflict between two sub-tasks.
• Score: 21.72593483340187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 suboptimal for capturing the complex spatial context and fine-grained identity cues necessary 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. In this paper, we propose DiffPS (Diffusion Prior Knowledge for Person Search), a novel framework that leverages a 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.

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