Deep hashing methods have achieved tremendous success in cross-modal retrieval, due to
its low storage consumption and fast retrieval speed. In real cross-modal retrieval …

Hashing has become increasingly important for large-scale image retrieval. Recently, deep
supervised hashing has shown promising performance, yet little work has been done under …

Hashing has become increasingly important for large-scale image retrieval, of which the low
storage cost and fast searching are two key properties. However, existing methods adopt …

Benefiting from recent advances in deep learning, deep hashing methods have achieved
promising performance in large-scale image retrieval. To improve storage and …

Deep incremental hashing has become a subject of considerable interest due to its
capability to learn hash codes in an incremental manner, eliminating the need to generate …

Deep hashing methods have been intensively studied and successfully applied in large-
scale multimedia retrieval. In real-world scenarios, code length can not be set once for all if …

Cross-modal retrieval between videos and texts has attracted growing attention due to the
rapid growth of user-generated videos on the web. To solve this problem, most approaches …

Due to the advantages of low storage cost and fast retrieval efficiency, deep hashing
methods are widely used in cross‐modal retrieval. Images are usually accompanied by …

Recently, face image hashing has been proposed in large-scale face image retrieval due to
its storage and computational efficiency. However, owing to the large intra-identity variation …

The past decade, we have witnessed rapid progress in compact representation learning for
fast image retrieval. In the unsupervised scenario, product quantization (PQ) is one of the …