Multimodal deep learning systems that employ multiple modalities like text, image, audio,
video, etc., are showing better performance than individual modalities (ie, unimodal) …

Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …

Cross-modal hashing (CMH) has gained much attention due to its effectiveness and
efficiency in facilitating efficient retrieval between different modalities. Whereas, most …

Deep cross-modal retrieval techniques have recently achieved remarkable performance,
which also poses severe threats to data privacy potentially. Nowadays, enormous user …

Deep metric learning has become a key component of cross-modal retrieval. By learning to
pull the features of matched instances closer while pushing the features of mismatched …

Color fundus photography (CFP) and Optical coherence tomography (OCT) images are two
of the most widely used modalities in the clinical diagnosis and management of retinal …

Video corpus moment retrieval has become a hot topic recently, which aims to localize a
consequent video moments highly relevant to the given query language description from …

Hashing has been an emerging topic and has recently attracted widespread attention in
multi-modal similarity search applications. However, most existing approaches rely on …

Cross-modal retrieval tasks, which are more natural and challenging than traditional
retrieval tasks, have attracted increasing interest from researchers in recent years. Although …

While deep neural networks (DNNs) deliver state-of-the-art accuracy on various applications
from face recognition to language translation, it comes at the cost of high computational and …