Our experience of the world is multimodal-we see objects, hear sounds, feel texture, smell
odors, and taste flavors. Modality refers to the way in which something happens or is …

With the wide deployments of heterogeneous networks, huge amounts of data with
characteristics of high volume, high variety, high velocity, and high veracity are generated …

This paper addresses the problem of learning similarity-preserving binary codes for efficient
similarity search in large-scale image collections. We formulate this problem in terms of …

Deep learning has emerged as a potential tool for crop yield prediction, allowing the model
to automatically extract features and learn from the datasets. Meanwhile, smart farming …

Deformable image registration is a fundamental task in medical image processing. Among
its most important applications, one may cite: 1) multi-modality fusion, where information …

Due to its low storage cost and fast query speed, cross-modal hashing (CMH) has been
widely used for similarity search in multimedia retrieval applications. However, most existing …

Thanks to the success of deep learning, cross-modal retrieval has made significant progress
recently. However, there still remains a crucial bottleneck: how to bridge the modality gap to …

Image Retrieval is a fundamental task of obtaining images similar to the query one from a
database. A common image retrieval practice is to firstly retrieve candidate images via …

Recently, multi-view representation learning has become a rapidly growing direction in
machine learning and data mining areas. This paper introduces two categories for multi …

The explosive growth in Big Data has attracted much attention in designing efficient indexing
and search methods recently. In many critical applications such as large-scale search and …