Deep Unsupervised Momentum Contrastive Hashing for Cross-modal Retrieval

Unsupervised cross-modal hashing (UCMH) methods often start from the similarity of sample features and design a reconstruction loss to achieve similarity preservation. However, these methods suffer from inaccurate similarity problems, because different feature representations may share similar semantic information. In this paper, we propose Deep Unsupervised Momentum Contrastive Hashing (DUMCH). Specifically, we introduce momentum contrastive learning for unsupervised cross-modal hashing, which allows us to flexibly define a robust loss by comparing positive and negative samples. Moreover, in order to achieve similarity retention of hash codes in Hamming space and fully utilize the potential of contrastive learning in Hamming space, we remove the L2 normalization corresponding to cosine similarity and design a novel normalization method called hash normalization, which has been proved to greatly improve the model performance. We conducted extensive experiments on three datasets, and the experimental results demonstrate the superiority of DUMCH.