Unsupervised deep hashing has demonstrated significant advancements with the
development of contrastive learning. However, most of previous methods have been …

Deep Hashing (DH) has emerged as an indispensable technique for fast image search in
recent years. To deploy DH on resource-limited devices, the Binary Neural Network (BNN) …

As a highly promising technique in the field of similarity search, the hashing-based image
retrieval algorithm has received continued attention in recent years because of its strong …

Driven by the high nonlinearity of deep neural networks, deep hashing has achieved the
pictured great potential in cross-modal retrieval applications, significantly bridging the …

Deep incremental hashing methods require a large number of original training samples to
preserve old knowledge. However, the old training samples are not always available. This …

Asymmetric image retrieval methods have drawn much attention due to their effectiveness in
resource-constrained scenarios. They try to learn two models in an asymmetric paradigm, ie …

Video hashing is a technique of encoding videos into binary vectors, facilitating efficient
video storage and high-speed computation. Current approaches to video hashing …

Hashing has received significant interest in large-scale data retrieval due to its outstanding
computational efficiency. Of late, numerous deep hashing approaches have emerged, which …

Similarity search is essential in current artificial intelligence applications and widely utilized
in various fields, such as recommender systems. However, the exponential growth of data …

With the emergence of AI generated content, cross-modal retrieval of 2D and 3D data has
obtained increasing research attention. In practical applications, massive amounts of 2D and …