PEOCH: Online Cross-Modal Hashing with Semi-Supervised Streaming Data Driving Prototype Evolution

The exponential growth of streaming multi-modal data presents critical challenges for cross-modal retrieval: distribution shifts, modality gap, and scarce labels. Semi-supervised online cross-modal hashing has gained increasing interest due to its ability to encode complex streaming data and update hash functions simultaneously. Nevertheless, existing methods can hardly generate high-quality unsupervised hash codes, which fundamentally limits diversity and flexibility during the retrieval process. To this end, we propose a novel method named Prototype Evolution Online Cross-modal Hashing (PEOCH). By driving prototype evolution with semi-supervised streaming data, precise and stable hash codes are generated for both labeled and unlabeled data. Specifically, two prototype updates with stability guarantee are conducted: labeled samples push semantic knowledge into the supervised prototypes, while unlabeled samples perform clustering to generate unsupervised prototypes. Simultaneously, a co-optimization mechanism is designed to ensure the prototypes continuously evolve and preserve the consistency of the entire streaming data. Besides, an elasticity regularizer integrates discriminability and smoothness constraints, improving the reliability of prototypes. Extensive experiments on three benchmark datasets demonstrate that PEOCH outperforms state-of-the-art methods, achieving an average improvement of 6.7% in mAP@all across various retrieval tasks.