Doubly Contrastive Learning for Source-Free Domain Adaptive Person Search

Abstract

Domain Adaptive Person Search (DAPS) aims to improve the generalization capability of person search models by training on both labeled source data and unlabeled target data, which is not that practical in real-world applications considering the storage/transmission costs and the privacy of source data. In this paper, we investigate a more practical and efficient person search setting, Source-Free Domain Adaptive Person Search (SFDA-PS), which seeks to generalize an existing source person search model to any unseen domain without requiring source data. Considering the absence of effective annotations in SFDA-PS, we propose a Doubly Contrastive Learning (DCL) method to adapt the target domain knowledge to the source model in a mutual learning and contrastive learning way. Specifically, we employ a mutual learning-based mean-teacher model as our baseline to incorporate target domain knowledge by pursuing prediction consistency between the teacher and student. Then, a Relation-embedded Contrastive (ReC) learning strategy is introduced to the detection head to ensure semantic consistency among proposals related to the same person while maintaining semantic distinction among proposals from different categories or persons. Furthermore, a Memory-aided Constrative (MaC) learning strategy is integrated into the re-identification (Re-ID) head to enhance its discriminative capability on target person embeddings. Extensive experiments on existing state-of-the-art person search models and two widely used benchmarks demonstrate the superiority of the proposed SFDA-PS task, as well as our proposed DCL.