Deep hashing has been widely used for large-scale cross-modal retrieval benefited from the
low storage cost and fast search speed. However, most existing deep supervised methods …

Although hash function learning algorithms have achieved great success in recent years,
most existing hash models are off-line, which are not suitable for processing sequential or …

Despite significant progress in supervised hashing, there are three common limitations of
existing methods. First, most pioneer methods discretely learn hash codes bit by bit, making …

Cross-modal hashing has attracted much attention in the large-scale multimedia search
area. In many real applications, labels of samples have hierarchical structure which also …

Recently, deep supervised hashing methods have shown state-of-the-art performance by
integrating feature learning and hash codes learning into an end-to-end network to generate …

Due to the high retrieval efficiency and low storage cost for cross-modal search tasks, cross-
modal hashing methods have attracted considerable attention from the researchers. For the …

Supervised hashing methods have achieved more promising results than unsupervised
ones by leveraging label information to generate compact and accurate hash codes. Most of …

Supervised hashing methods have attracted much attention in these years. However, most
existing supervised hashing algorithms have some of the following problems. First, most of …

Deep supervised hashing has emerged as an effective solution to large-scale semantic
image retrieval problems in computer vision. Convolutional neural network-based hashing …

Cross-modal hashing similarity retrieval plays dual roles across various applications
including search engines and autopilot systems. More generally, these methods also known …