Unsupervised cross-modal hashing (UCMH) has become a hot topic recently. Current
UCMH focuses on exploring data similarities. However, current UCMH methods calculate …

With the development of web technology, multi-modal or multi-view data has surged as a
major stream for big data, where each modal/view encodes individual property of data …

The focus of this survey is on the analysis of two modalities of multimodal deep learning:
image and text. Unlike classic reviews of deep learning where monomodal image classifiers …

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 …

With the rapid development of science and technology, all types of mixed media contain
large amounts of data. Traditional single multimedia data can no longer satisfy daily …

With the rapid development of deep neural networks, cross-modal hashing has made great
progress. However, the information of different types of data is asymmetrical, that is to say, if …

Multimodal sentiment analysis is one of the most attractive interdisciplinary research topics
in artificial intelligence (AI). Different from other classification issues, multimodal sentiment …

Recently, numerous proxy hash code based methods, which sufficiently exploit the label
information of data to supervise the training of hashing models, have been proposed …

The cross-media hashing retrieval generally encodes multimedia data into a common binary
hash space, which can effectively measure the correlation between samples from different …

Due to the explosive growth of multimedia data in recent years, cross-media hashing (CMH)
approaches have recently received increasing attention. To learn the hash codes, most …