Relevance-Resolution Transfer via Scale-Decomposable Fractional Diffusion for Multi-Length Cross-Modal Hash Retrieval

Cross-modal hashing enables efficient retrieval by encoding heterogeneous data into compact binary codes. Recent methods increasingly exploit fine-grained relations encoded in multi-label training structure; however, they do not explicitly ensure that these relations are preserved as consistent candidate rankings in finite, multi-length Hamming spaces, revealing a relevance resolution bottleneck (RRB). To address the RRB, we propose MultiBit for transferring relevance resolution from multi-label structure to multi-length Hamming spaces. On the one hand, MultiBit constructs a scale-decomposable fractional relation teacher from dataset-level label co-occurrence and label specificity, modeling dependencies from local to long-range over continuous diffusion scales to produce query-conditioned relevance targets. On the other hand, it maps discretized diffusion scales and their contribution weights to scale-aware bit subblocks, organizes multiple code lengths as nested prefixes, and aligns their Hamming candidate rankings with the teacher relations. Experiments on four benchmarks consistently demonstrate improvements in retrieval accuracy, verifying the relevance-resolution advantages brought by scale-decomposable fractional diffusion in multi-length cross-modal hashing.