Improve Unsupervised Deep Hashing Via Masked Contrastive Learning

Unsupervised hashing method aims to generate compact binary hash codes for images without label supervision. Existing unsupervised hashing methods usually learn binary hash codes by reconstructing input data or preserving similarity structures. However, these methods will either force the hash code to retain a large amount of redundant information or will learn a similarity structure with noise due to biased prior knowledge, resulting in poor retrieval performance. In this paper, we introduce a novel unsupervised hashing method called Masked Contrastive Hashing (MCH). Specifically, to maximally preserve meaningful semantic information into the binary hash code, MCH adopts an encoder-decoder structure and extracts the binary representation from the random masked image to reconstruct the original image. Furthermore, MCH maximizes the consistency of the enhanced views of the same image while minimizing the consistency of different images to establish the similarity relationship between images, which is helpful to generate hash codes that are more suitable for retrieval tasks. Extensive experiments show that the proposed MCH significantly outperforms existing state-of-the-art methods on several benchmark datasets.