Graph-Propagated Multi-Scale Hashing with Contrastive Learning for Unsupervised Cross-Modal Retrieval

This paper introduces Graph-Propagated Multi-Scale Hashing with Contrastive Learning (GPMCL), a novel unsupervised cross-modal hashing framework designed to address the semantic deficiency in large-scale unlabeled multimodal data. GPMCL first constructs an initial similarity matrix via cross-modal graph propagation, effectively capturing potential inter-modal relationships. A multi-scale enhancement strategy is then employed to integrate both local and global similarities, resulting in a more informative and robust similarity representation. To adaptively distinguish sample relationships, a Gaussian Mixture Model (GMM) is utilized to determine dynamic thresholds. Additionally, contrastive learning is incorporated in the feature space to enhance intra-class compactness and inter-class separability. Extensive experiments conducted on three public benchmark datasets demonstrate that GPMCL consistently outperforms existing state-of-the-art unsupervised cross-modal hashing methods in terms of retrieval performance. These results validate the effectiveness and generalization capability of the proposed method, highlighting its potential for practical cross-modal retrieval applications.