Semi-supervised Domain Adaptive Retrieval via Discriminative Hashing Learning

Domain adaptive image retrieval (DAR) aims to train the model with well-labeled source domain and target images in order to retrieve source instances given query target samples from the identical category space. However, the practical scenario hinders to manually annotate all retrieved images due to huge labeling cost. Motivated by the realistic demand, we firstly define the semi-supervised domain adaptive retrieval (SDAR) problem, assuming the database includes a small proportion annotated source images and abundant unlabeled ones. To overcome the challenging SDAR, this paper propose a novel method named Discriminative Hashing learning (DHLing) which mainly includes two modules, i.e., domain-specific optimization and domain-invariant memory bank. Specifically, the first component explores the structural knowledge of samples to predict the unlabeled images with pseudo labels to achieve hash coding consistency. While, the second one attempts to construct the domain-invariant memory bank to guide the feature generation and achieve cross-domain alignment. Experimental results on several popular cross-domain retrieval benchmarks illustrate the effectiveness of our proposed DHLing on both conventional DAR and new SDAR scenarios by comparing with the state-of-the-art retrieval methods.