Due to the low storage cost and high computation efficiency of hashing, cross-modal hashing has been attracting widespread attention in recent years. In this paper, we investigate how supervised cross-modal hashing (CMH) benefits from multi-label and contrastive learning (CL) by overcoming the following two challenges: (i) how to combine multi-label and supervised contrastive learning to consider diverse relationships among cross-modal instances, and (ii) how to reduce the sparsity of multi-label representation so as to improve the similarity measurement accuracy. To this end, we propose a novel cross-modal hashing framework, dubbed Multi-Label Weighted Contrastive Hashing (MLWCH). This framework involves compact consistent similarity representation, a new designed multi-label similarity calculation method that efficiently reduces the sparsity of multi-label by reducing redundant zero elements. Furthermore, a novel multi-label weighted contrastive learning strategy is developed to significantly improve hashing learning by assigning similarity weight to positive samples under both linear and non-linear similarities. Extensive experiments and ablation analysis over three benchmark datasets validate the superiority of our MLWCH method, especially over several outstanding baselines.

Unsupervised cross-modal hashing is a topic of considerable interest due to its advantages in terms of low storage costs and fast retrieval speed. Despite the impressive achievements of existing solutions, two challenges remain unaddressed: (1) Semantic similarity obtained without supervision is not accurate enough, and (2) the preservation of similarity structures lacks effectiveness due to the neglect of both global and local similarity. This paper introduces a new method, Multi-Grained Similarity Preserving and Updating (MGSPU), to tackle these challenges. To overcome the first challenge, MGSPU employs a newly designed strategy to update the semantic similarity matrix, effectively generating a high-confidence similarity matrix by eliminating noise in the original cross-modal features. For the second challenge, a novel multi-grained similarity preserving method is proposed, aiming to enhance cross-modal hash code learning by learning consistency in multi-grained similarity structures. Comprehensive experiments on two widely used datasets with nine state-of-the-art competitors validate the superior performance of our method in cross-modal hashing.