We are delighted to announce that one paper from the Perceptual Artificial Intelligence Lab (Perceptual AI Lab, Prof. Chanho Eom) has been accepted to the 2025 International Conference on Computer Vision (ICCV) [LINK].

Title: 

DiffPS: Leveraging Prior Knowledge of Diffusion Model for Person Search

Authors:

Giyeol Kim*, Sooyoung Yang*, Jihyong Oh, Myungjoo Kang, and Chanho Eom**

(*equal contribution) (**corresponding author)

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 back-

bones, 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.