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 …

Cross-media analysis and reasoning is an active research area in computer science, and a
promising direction for artificial intelligence. However, to the best of our knowledge, no …

Visual question answering (VQA) is challenging because it requires a simultaneous
understanding of both the visual content of images and the textual content of questions. The …

With the popularization of the Internet, permeation of sensor networks, emergence of big
data, increase in size of the information community, and interlinking and fusion of data and …

It is known that the inconsistent distributions and representations of different modalities, such
as image and text, cause the heterogeneity gap, which makes it very challenging to correlate …

Recently, convolutional neural network (CNN) visual features have demonstrated their
powerful ability as a universal representation for various recognition tasks. In this paper …

Text-video retrieval is one of the basic tasks for multimodal research and has been widely
harnessed in many real-world systems. Most existing approaches directly compare the …

In this paper, we present a new multimedia retrieval paradigm to innovate large-scale
search of heterogenous multimedia data. It is able to return results of different media types …

In this paper, we propose a new unsupervised spectral feature selection model by
embedding a graph regularizer into the framework of joint sparse regression for preserving …

The problem of cross-modal retrieval from multimedia repositories is considered. This
problem addresses the design of retrieval systems that support queries across content …