DSAC-Hash: Distribution-Similarity-Aware Cross-modal Hashing

Abstract

The rapid growth of online multimedia data has made cross-modal hashing crucial for efficient retrieval. Existing methods often fail to handle the heterogeneity of image and text data and lack sufficient semantic interaction, resulting in reduced retrieval accuracy. To address these issues, we introduce the DSAC-Hash framework, which includes an Innovative Semantic Interaction Aggregator (SIA) to refine inter- and intra-modal relationships, reducing semantic discrepancies and enhancing retrieval performance. Additionally, we present a unified weighted loss framework optimizes cross-modal similarity by incorporating weighted triplet, contrastive, and semantic loss functions, improving the quality of binary hash codes. These enhancements significantly boost image-to-text (I2T) and text-to-image (T2I) retrieval performance.Experiments on MS COCO, Mirflickr-25k, and NUS-Wide show that DSAC-Hash achieves state-of-the-art performance, with notable MAP improvements with at least: $4.59\sim 10.45\text{\%}$ (I2T) and $7.39\sim 12.96\text{\%}$ (T2I) on MS COCO, $1.52\sim 8.81\text{\%}$ (I2T) and $2.75\sim 7.34\text{\%}$ (T2I) on Mir-Flickr, and $4.78\sim 7.74\text{\%}$ (I2T) and $7.03\sim 9.42\text{\%}$ (T2I) on NUS-WIDE, confirming its robustness, scalability, and effectiveness in large-scale multimedia retrieval scenarios.