Label Prediction Inherited Hashing for Cross-Modal Retrieval: Applying Supervised Hashing to Unsupervised Tasks

摘要

Supervised cross-modal hashing has achieved remarkable progress in retrieving related items across different modalities. However, in practical applications, a significant portion of data remains unlabeled, such as online data on websites, which must be included for effective retrieval. To address this challenge, while maintaining the high accuracy and efficiency of supervised methods, few works have attempted to adapt existing supervised techniques to handle unsupervised tasks through a general modular approach. To this end, we introduce a novel cross-modal hashing method, termed Label Prediction Inherited Hashing (LPIH). Initially, LPIH leverages labeled data to learn high-quality general label functions using supervised methods. Subsequently, it inherits the existing hash codes from existing supervised methods to further refine the pseudo-label information. Finally, LPIH integrates the refined pseudo-label information with the existing hash functions to learn new hash functions specifically tailored for unsupervised tasks. Extensive experimental results on three public datasets demonstrate the superior performance of LPIH compared to state-of-the-art (SOTA) cross-modal hashing methods. Specifically, LPIH achieves an average precision improvement of 5% over SOTA methods, highlighting its effectiveness in bridging the gap between supervised and unsupervised learning in the context of cross-modal retrieval.