Cross Modal Hashing

Cross-modal hashing aims to efficiently retrieve data across different modalities (e.g., images and text) by encoding them into compact binary hash codes. Current research focuses on improving retrieval accuracy and efficiency, particularly for imbalanced datasets and scenarios with limited labeled data, employing techniques like contrastive learning, autoencoders, and vision-language pre-training models to learn robust and discriminative hash functions. These advancements are significant for large-scale multimedia retrieval applications, enabling faster search and reduced storage costs while addressing challenges like the modality gap and noisy data. The development of robust and efficient cross-modal hashing methods is crucial for various applications, including remote sensing and general multimedia search.