Distribution Consistency Guided Hashing for Cross-Modal Retrieval

With the massive emergence of multi-modal data, cross-modal retrieval (CMR) has become one of the hot topics. Thanks to fast retrieval and efficient storage, cross-modal hashing (CMH) provides a feasible solution for large-scale multi-modal data. Previous CMH methods always directly learn common hash codes to fuse different modalities. Although they have obtained some success, there are still some limitations: 1) These approaches often prioritize reducing the heterogeneity in multi-modal data by learning consensus hash codes, yet they could sacrifice modality-specific information. 2) They frequently utilize pairwise similarities to guide hashing learning and neglect class distribution correlations. To overcome these two issues, we propose a novel Distribution Consistency Guided Hashing (DCGH) framework. Specifically, we first learn the modality-specific representation to extract the private discriminative information. Further, we learn consensus hash codes from the private representation by consensus hashing learning, thereby merging the specifics with consistency. Finally, we propose distribution consistency learning to guide hash codes following a similar class distribution principle between multi-modal data, thereby exploring more consistent information. Lots of experimental results on four benchmark datasets demonstrate the effectiveness of our DCGH on both fully paired and partially paired CMR tasks. The code can be available at: https://github.com/sunyuan-cs/2024-MM-DCGH.