With the exponential surge in diverse multi-modal data, traditional uni-modal retrieval
methods struggle to meet the needs of users seeking access to data across various …

With the explosive growth of multimedia contents, multimedia retrieval is facing
unprecedented challenges on both storage cost and retrieval speed. Hashing technique can …

The current state-of-the-art image-sentence retrieval methods implicitly align the visual-
textual fragments, like regions in images and words in sentences, and adopt attention …

Cross-modal hashing that leverages hash functions to project high-dimensional data from
different modalities into the compact common hamming space, has shown immeasurable …

Inspired by the powerful representation capability of deep neural networks, deep cross-
modal hashing methods have recently drawn much attention and various deep cross-modal …

Social networks allow users to actively upload images and descriptive tags, which has led to
an explosive growth in the number of social images. Multi-view hashing is an efficient …

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 …

Cross-modal retrieval aims to identify relevant data across different modalities. In this work,
we are dedicated to cross-modal retrieval between images and text sentences, which is …

Multi-modal hashing technology can support large-scale multimedia retrieval well, because
of its fast query speed and low storage consumption. Although many multi-modal hashing …

Multi-modal hashing (MMH) encodes multi-modal data into latent hash code, and has been
widely applied for efficient large-scale multi-modal retrieval. In practice it is common that …