Signal Processing - November 2017 - 96

DEEP LEARNING FOR VISUAL UNDERSTANDING

Dhanesh Ramachandram and
Graham W. Taylor

Deep Multimodal Learning
A survey on recent advances and trends

T

he success of deep learning has been a catalyst to solving
increasingly complex machine-learning problems, which
often involve multiple data modalities. We review recent
advances in deep multimodal learning and highlight the state-of
the art, as well as gaps and challenges in this active research
field. We first classify deep multimodal learning architectures
and then discuss methods to fuse learned multimodal representations in deep-learning architectures. We highlight two areas
of research-regularization strategies and methods that learn or
optimize multimodal fusion structures-as exciting areas for
future work.

Introduction

©ISTOCKPHOTO.COM/ZAPP2PHOTO

Digital Object Identifier 10.1109/MSP.2017.2738401
Date of publication: 13 November 2017

96

Neural networks have made an impressive resurgence in recent
years, after long-standing concerns about the ability to train
deep models were successfully abated by a pioneering group
of researchers who leveraged advances in algorithms, data, and
computation [1]. This active research area now interests researchers in academia, but also industry, and it has resulted in stateof-the-art performance for many practical problems, especially
in areas involving high-dimensional unstructured data such as in
computer vision, speech, and natural language processing.
With the undeniable success of deep learning in the visual
domain, the natural progression of deep-learning research
points to problems involving larger and more complex multimodal data. Such multimodal data sets consist of data from
different sensors observing a common phenomena, and the
goal is to use the data in a complementary manner toward
learning a complex task. One of the main advantages of deep
learning is that a hierarchical representation can be automatically learned for each modality, instead of manually designing or handcrafting modality-specific features that are then
fed to a machine-learning algorithm.
The goal of this article is to provide a comprehensive survey of the state of the art in deep multimodal learning and suggest future research directions by highlighting advances, gaps,
and challenges in this active field. We believe this review is
timely given the increasing number of deep-learning techniques

IEEE SIGNAL PROCESSING MAGAZINE

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November 2017

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©2017 British Crown Copyright



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Table of Contents for the Digital Edition of Signal Processing - November 2017

Signal Processing - November 2017 - Cover1
Signal Processing - November 2017 - Cover2
Signal Processing - November 2017 - 1
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Signal Processing - November 2017 - Cover3
Signal Processing - November 2017 - Cover4
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