Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

Learning multimodal representations involves integrating information from multiple
heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world …

Face detection is one of the most studied topics in the computer vision community. Much of
the progresses have been made by the availability of face detection benchmark datasets …

In this paper, we review the background and state-of-the-art of big data. We first introduce
the general background of big data and review related technologies, such as could …

Perceiving a scene most fully requires all the senses. Yet modeling how objects look and
sound is challenging: most natural scenes and events contain multiple objects, and the …

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …

We address the challenge of sentiment analysis from visual content. In contrast to existing
methods which infer sentiment or emotion directly from visual low-level features, we propose …

This paper introduces a visual sentiment concept classification method based on deep
convolutional neural networks (CNNs). The visual sentiment concepts are adjective noun …