Semantic Guidance for Boundary Aware Cross-Modal Hashing under Noisy Supervision
When labels are corrupted by noise, cross-modal hashing often fails to maintain reliable semantic neighborhoods and clear decision boundaries in the Hamming space. Existing robust hashing methods mainly improve the reliability of samples or pairwise supervision, while the class-level semantics contained in multi-label...