In recent years, cross-modal retrieval has drawn much attention due to the rapid growth of
multimodal data. It takes one type of data as the query to retrieve relevant data of another …

With the exponential surge in diverse multimodal data, traditional unimodal retrieval
methods struggle to meet the needs of users seeking access to data across various …

Due to the flexibility in modelling data heterogeneity, heterogeneous information network
(HIN) has been adopted to characterize complex and heterogeneous auxiliary data in …

Data embedding is used in many machine learning applications to create low-dimensional
feature representations, which preserves the structure of data points in their original space …

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 …

Due to its low storage cost and fast query speed, hashing has been widely adopted for
similarity search in multimedia data. In particular, more and more attentions have been …

Due to the storage and retrieval efficiency, hashing has been widely applied to approximate
nearest neighbor search for large-scale multimedia retrieval. Cross-modal hashing, which …

Learning-based hashing algorithms are “hot topics” because they can greatly increase the
scale at which existing methods operate. In this paper, we propose a new learning-based …

With the advent and popularity of social network, more and more users like to share their
experiences, such as ratings, reviews, and blogs. The new factors of social network like …

With the rapid development of information storage and networking technologies, quintillion
bytes of data are generated every day from social networks, business transactions, sensors …