Multimedia retrieval plays an indispensable role in big data utilization. Past efforts mainly
focused on single-media retrieval. However, the requirements of users are highly flexible …

In this paper, we review recent emerging theoretical and technological advances of artificial
intelligence (AI) in the big data settings. We conclude that integrating data-driven machine …

While deep convolutional neural networks (CNNs) have shown a great success in single-
label image classification, it is important to note that most real world images contain multiple …

Hashing based methods have attracted considerable attention for efficient cross-modal
retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to …

Hashing methods have been extensively applied to efficient multimedia data indexing and
retrieval on account of the explosion of multimedia data. Cross-modal hashing usually …

By transforming data into binary representation, ie, Hashing, we can perform high-speed
search with low storage cost, and thus, Hashing has collected increasing research interest in …

Nowadays, cross-modal retrieval plays an important role to flexibly find useful information
across different modalities of data. Effectively measuring the similarity between different …

We present an image captioning framework that generates captions under a given topic. The
topic candidates are extracted from the caption corpus. A given image's topics are then …

The heterogeneity-gap between different modalities brings a significant challenge to
multimedia information retrieval. Some studies formalize the cross-modal retrieval tasks as a …

The selection of semantic concepts for modal construction and data collection remains an
open research issue. It is highly demanding to choose good multimedia concepts with small …