DANCE: Learning A Domain Adaptive Framework for Deep Hashing

Abstract

This paper studies unsupervised domain adaptive hashing, which aims to transfer a hashing model from a label-rich source domain to a label-scarce target domain. Current state-of-the-art approaches generally resolve the problem by integrating pseudo-labeling and domain adaptation techniques into deep hashing paradigms. Nevertheless, they usually suffer from serious class imbalance in pseudo-labels and suboptimal domain alignment caused by the neglection of the intrinsic structures of two domains. To address this issue, we propose a novel method named unbiaseD duAl hashiNg Contrastive lEarning (DANCE) for domain adaptive image retrieval. The core of our DANCE is to perform contrastive learning on hash codes from both instance level and prototype level. To begin, DANCE utilizes label information to guide instance-level hashing contrastive learning in the source domain. To generate unbiased and reliable pseudo-labels for semantic learning in the target domain, we uniformly select samples around each label embedding in the Hamming space. A momentum-update scheme is also utilized to smooth the optimization process. Additionally, we measure the semantic prototype representations in both source and target domains and incorporate them into a domain-aware prototype-level contrastive learning paradigm, which enhances domain alignment in the Hamming space while maximizing the model capacity. Experimental results on a number of well-known domain adaptive retrieval benchmarks validate the effectiveness of our proposed DANCE compared to a variety of competing baselines in different settings.