The paper proposes a Fast One-stage Unsupervised person Search (FOUS) framework for the task of unsupervised domain adaptive person search. The key highlights are:
FOUS introduces a prototype-guided labeling method to efficiently assign soft labels to unlabeled target domain samples, replacing the computationally expensive clustering algorithms used in previous methods.
FOUS designs an Attention-based Domain Alignment Module (ADAM) that can align the feature representations across domains for both detection and re-identification tasks, while also reducing the adverse impact of low-quality candidate boxes from unsupervised detection.
FOUS adopts a label-flexible training network with an adaptive selection strategy to gradually refine the coarse labels assigned by the prototype-guided method.
Without any auxiliary labels in the target domain, FOUS achieves state-of-the-art performance on two benchmark datasets, CUHK-SYSU and PRW, while significantly reducing the computational cost and inference time compared to previous methods.
Extensive experiments validate the effectiveness of the proposed components, including the attention module, prototype-guided labeling, and label-flexible training, in improving the overall performance of the unsupervised domain adaptive person search task.
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