On-Demand Hash Expansion for Growing Cross-Modal Databases

In growing cross-modal databases, adapting representations to continuous data arrivals while maintaining historical search indexes poses a persistent challenge. Although new arrivals provide supervision to improve representations, materializing newly learned hash bits requires an encoding and writing pass over historical entries. We introduce On-Demand Elastic Hashing (ODEH), which expands ranking capacity without widening the stored search index. ODEH retrieves candidates via a compact base index and ranks them by complete Hamming distance using residual bits generated on demand from frozen backing features. This decoupled design preserves lightweight index scans while trading historical code writes for retained feature storage and candidate computation. Residual blocks are trained against candidate ranking boundaries with earlier bits frozen. A two-tier rule selects the shortest extension meeting a prescribed Top-K quality target, falling back to statistically tested improvement when no extension meets the target. Experiments on COCO, NUS-WIDE, and MIRFlickr compare ODEH with six external baselines using shared frozen features. ODEH achieves the highest mean in five of six dataset–direction pairs for each of Precision@10, nDCG@10, and query success, while maintaining a 32-bit base index.