Reliable Supervision and Semantic Initialization for Noisy Multi-Label Cross-Modal Hashing Retrieval

Supervised multi-label cross-modal hashing relies on label information to construct semantic relations across heterogeneous modalities. However, false-positive and missing-positive labels introduce spurious positive relations and missing associations, respectively, directly distorting cross-modal semantic alignment and degrading binary representation learning. Existing robust methods mainly focus on identifying, reweighting, or correcting noisy supervision, but often underexploit the fine-grained multi-label structure after correction. Consequently, corrected supervision is not fully preserved across continuous semantic relation modeling and discrete hash optimization. To address this issue, we propose Reliability-Based Cross-modal Deep Hashing (RBCDH), which unifies supervision reliability modeling with multi-resolution semantic learning. RBCDH estimates instance–class-level label reliability by integrating intra-modal neighborhood label support with cross-modal consistency, and performs controlled bidirectional correction to suppress false positives while recovering missing positives. To preserve fine-grained semantics after correction, we construct continuous Soft-Jaccard relations from corrected label overlaps, providing a reliability-aware initialization before discrete optimization. We further derive binary reliable relations from the same corrected supervision to constrain the final hash representations, enabling reliable semantics to be consistently exploited across continuous and discrete spaces. Experiments on NUS-WIDE TC-21, and MM-IMDb under different noise levels and code lengths show that RBCDH consistently outperforms the baseline methods in overall cross-modal retrieval performance under noisy supervision.