Fast One-Stage Unsupervised Domain Adaptive Person Search

TL;DR

This paper introduces FOUS, a fast, end-to-end unsupervised person search method that aligns domains and labels without iterative clustering, achieving state-of-the-art results efficiently.

Contribution

The paper proposes a novel one-stage unsupervised person search framework integrating domain and label adaptation without iterative clustering.

Findings

Achieves SOTA performance on CUHK-SYSU and PRW datasets.

Reduces model complexity by eliminating multi-stage clustering.

Effectively aligns domains and refines labels in an end-to-end manner.

Abstract

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.