A novel strategy to balance the results of cross-modal hashing

Highlights：

• This paper reveals the problem of unbalanced semantic information of different feature representations in cross-modal retrieval and explores the semantic augmentation for cross-modal retrieval.

• A semantic augmentation strategy based on the intermediate semantic space is proposed to augment the semantic information of the modality data with weak semantics.

• Extensive experiments on four datasets using typical cross-modal hashing methods, as well as real-valued, partial-paired, semi-paired, and completely unpaired cross-modal retrieval approaches are conducted to evaluate the effectiveness of semantic augmentation, and the results show that the gap between cross-modal retrieval results can be decreased substantially.