Deep Incremental Cross-Modal Hashing Network for Fast Retrieval

Cross-modal hashing techniques provide an effective method for large-scale cross-modal search due to their ability to handle multiple data types and their efficient storage and computation performance. To achieve excellent performance, deep supervised cross-modal hashing methods require extensive training data from various classes. However, when new classes emerge in the database, existing cross-modal hashing methods typically need to retrain the image and text encoders and regenerate hash codes for all data, which is impractical for large-scale retrieval systems. In this paper, we introduce an innovative cross-modal incremental hashing framework called Deep Incremental Cross-Modal Hashing Network (DICMHN), which can learn hash codes incrementally. The DICMHN framework is capable of directly learning the hash codes of newly emerging images and texts while maintaining the integrity of existing hash codes. Moreover, this framework ensures the accuracy of query results by modeling the correlation between query images and matching texts, as well as between query texts and matching images, while preserving the distinctions between images and texts. Extensive experiments conducted on multiple cross-modal benchmark datasets demonstrate that our proposed DICMHN framework significantly reduces training time and outperforms existing state-of-the-art methods.