Signal Processing - November 2017 - 101

Table 3. Diverse applications of multimodal deep learning.
Reference

Year

Modalities

Problem

Fusion Method

Model

Architecture

Ngiam et al. [6]

2011

Audio,video

Speech classification

Intermediate

Generative

Sparse RBM

Srivastava and
- alakhutdinov [30]
S

2012

Image, text

Image annotation

Intermediate

Generative

DBN

Cao et al. [31]

2014

Medical images, t-extual
descriptions

Content-based medical
image retrieval

Intermediate

Generative

DBM

Liang et al. [32]

2015

Gene expression, DNA
methylation, and drug
response

Cancer subtype clustering

Intermediate

Generative

DBM

Valada et al. [15]

2016

Multispectral imagery

Semantic segmentation

Early

Discriminative

FCNN

Simonyan and
-Zisserman [33]

2014

Image and optical flow

Action recognition

Late

Discriminative

CNN

Kahou et al. [11]

2015

Video, audio

Emotion recognition

Late

Discriminative

CNN, RNN, SVM,
and AE

Liu et al. [20]

2015

MRI, PET

Medical diagnosis

Intermediate

Discriminative

Stacked AE, SVM

Poria et al. [34]

2015

Video, audio, text

Sentiment analysis

Intermediate,
late

Discriminative

CNN, SVM

Lenz et al. [28]

2015

Intensity, depth video

Robotic grasping

Intermediate

Discriminative

Stacked AE and
MLP

Jain et al. [35]

2016

Video features, GPS coordinates, vehicle dynamics

Driver activity anticipation

Intermediate

Discriminative

LSTM

by minimizing some regularized loss function. Such models
compose the bulk of the proposed models for multimodal learning, while tasks include classification or recognition for a variety of problem domains.
In addition to the aforementioned active research problems,
image captioning and VQA [36], both of which combine natural language processing and high-level scene interpretation by
machine-learning algorithms, have garnered active research
interest. In deep image captioning, the model is required to
generate a textual description of image content, and this could
be achieved by using both discriminative techniques [37], [38]
and generative approaches [39]. On the other hand, VQA typically requires the model to answer complex questions based
on image content, which is a generative task. This problem can
also be cast into a discriminative setting (e.g., multiple choice
questions)  [40]. Recently,  Kim et  al. [41] extended the highly successful deep residual network model for a multimodal
VQA problem. As multiple modalities may have correlations,
the authors carefully designed joint residual mappings across
modalities and achieved state-of-the-art results for VQA.
Discriminative deep multimodal learning models have
also been proposed for human activity recognition. With the
cheap availability of RGB-depth (RGB-D) cameras and ubiquity of smartphones with numerous sensors, deep multimodal
learning architectures that involve from four to five modalities have been reported. These problems involve temporal
data (video, joint motion, audio), and it is essential that spatiotemporal dependencies be learned effectively. To capture
temporal structures and relationships, deep multimodal learning approaches typically use temporal components such as
LSTMs or hidden Markov models combined with visual rep-

resentation learning layers like CNNs or 3-D-CNNs [42], [43].
These models have benefited from the combination of CNNs
and recurrent layers that can collectively capture spatiotemporal relationships.
There are also instances of work where generative models have been adapted to perform discriminative tasks. For
example, a discriminative variant of the RBM [building block
of deep belief networks (DBNs) and deep Boltzmann machines]
was proposed by Larochelle and Bengio [44]. Other discriminative models have previously been mentioned while discussing
application areas in the sections "Human Activity Recognition,"
"Medical Applications," and "Autonomous Systems." In addition, Table  3 also lists examples of discriminative models for
other multimodal problems. While discriminative models excel
at the task of classification or regression, they cannot cope when
there are missing data or modalities. Discriminative models also
require a large set of labeled data, which could be expensive to
obtain in certain applications. Next, we review deep generative
multimodal models, which offer some advantages, considering
the drawbacks of discriminative models, in the context of learning multimodal representations.

Generative models
Deep generative models typically characterize the high-order
correlation properties of the observed or visible data for pattern analysis or synthesis purposes. They can also be used to
characterize the joint statistical distributions of the visible data
and their associated classes. Generative models like DBNs
can also be used for classification and regression tasks by
exploiting their capability to learn (unsupervised) from unlabeled data and fine-tuned in a discriminative setting using the

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

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