  Unsupervised person search aims to localize a particular target person from a
gallery set of scene images without annotations, which is extremely challenging
due to the unexpected variations of the unlabeled domains. However, most
existing methods dedicate to developing multi-stage models to adapt domain
variations while using clustering for iterative model training, which
inevitably increases model complexity. To address this issue, we propose a Fast
One-stage Unsupervised person Search (FOUS) which complementary integrates
domain adaptaion with label adaptaion within an end-to-end manner without
iterative clustering. To minimize the domain discrepancy, FOUS introduced an
Attention-based Domain Alignment Module (ADAM) which can not only align various
domains for both detection and ReID tasks but also construct an attention
mechanism to reduce the adverse impacts of low-quality candidates resulting
from unsupervised detection. Moreover, to avoid the redundant iterative
clustering mode, FOUS adopts a prototype-guided labeling method which minimizes
redundant correlation computations for partial samples and assigns noisy coarse
label groups efficiently. The coarse label groups will be continuously refined
via label-flexible training network with an adaptive selection strategy. With
the adapted domains and labels, FOUS can achieve the state-of-the-art (SOTA)
performance on two benchmark datasets, CUHK-SYSU and PRW. The code is available
at https://github.com/whbdmu/FOUS.
