Dominant Person Search methods aim to localize and recognize query persons in
a unified network, which jointly optimizes two sub-tasks, \ie, detection and
Re-IDentification (ReID). Despite significant progress, two major challenges
remain: 1) Detection-prior modules in previous methods are suboptimal for the
ReID task. 2) The collaboration between two sub-tasks is ignored. To alleviate
these issues, we present a novel Person Search framework based on the Diffusion
model, PSDiff. PSDiff formulates the person search as a dual denoising process
from noisy boxes and ReID embeddings to ground truths. Unlike existing methods
that follow the Detection-to-ReID paradigm, our denoising paradigm eliminates
detection-prior modules to avoid the local-optimum of the ReID task. Following
the new paradigm, we further design a new Collaborative Denoising Layer (CDL)
to optimize detection and ReID sub-tasks in an iterative and collaborative way,
which makes two sub-tasks mutually beneficial. Extensive experiments on the
standard benchmarks show that PSDiff achieves state-of-the-art performance with
fewer parameters and elastic computing overhead.

基于扩散模型的 PSDiff 的人物搜索框架能够通过协同去噪的方式优化检测和重新识别子任务，取得了超越现有方法的最先进性能。