Signal Processing - November 2017 - 99

Table 2. Multimodal learning data sets and public multimodal machine-learning challenges.
Data Set

Modalities

Problem

Reference

Year

UTD-MHAD

Depth and inertial sensor data

Human action recognition

Chen et al. [93]

2015

ChaLearn looking at people

RGB-D, audio, skeletal pose

Human activity recognition

Escalera et al. [94]

2014

Berkeley MHAD

Multiviewpoint RGB-D and skeletal pose
data

Human activity recognition

Ofli et al. [95]

2013

MHRI data set

Chest, top RGB-D, face, video, and audio

Human-robot interaction

Pablo et al. [96]

2016

H-MOG

Nine smartphone sensors and interaction
data

Continuous authentication in smartphones

Sitová et al. [12]

2016

RECOLA

Audio, visual, and physiological

Emotion recognition

Ringeval et al. [97]

2013

MHEALTH

Accelerometer, electrocardiogram, magnetometer, and gyroscopes

Health monitoring

Banos et al. [98]

2015

Pinterest Multimodal

Images and text (40M)

Multimodal word embeddings

Mao et al. [99]

2016

MM-IMDb

Video, images, and text metadata

Movie genre prediction

Arevalo et al. [100]

2017

FCVID

Video and audio

Action recognition

Jiang et al. [101]

2017

KITTI

Stereo gray- and color video, 3-D-LIDAR,
inertial and GPS navigation data

Autonomous driving

Geiger et al. [91]

2017

KinectFaceDB

RGB-D and facial landmarks

Face recognition

Min et al. [102]

2014

Oxford RobotCar

Six cameras, LIDAR, GPS, and inertial
-navigation data

Autonomous driving

Maddern et al. [92]

2016

Multimodal BRATS

T2-, FLAIR-, post-Gadolinium T1-MRI,
- erfusion, and diffusion MRI and MRSI
p

Brain tumor segmentation

Menze et al. [103]

2015

RGB-D: RGB-depth

exhibit highly complex behavior in social settings, it is only
natural that multimodal data are required for machine-learning
algorithms to classify, or "understand," their human behavior.
Not surprisingly, we found that many works in deep multimodal fusion reported in recent years have focused on multimedia data that typically involve modalities such as audio, video,
depth, and skeletal motion information. Multimodal deep-learning methods have been applied to various problems involving
human activity such as action recognition (an activity can be
composed of two or more sequences of shorter actions), gesture recognition [8], gaze-direction estimation [9], face recognition [10], and emotion recognition [11]. The ubiquity of mobile
smartphones, which have no fewer than ten sensors, has given
rise to novel applications that involve multimodal data such as
continuous biometric authentication [12] and activity recognition [13]. Related subfields of research include human pose estimation [14] and semantic scene understanding [15].
We expect that the deep-learning research community will
continue to focus on these problems in the foreseeable future.
This is evidenced not only by the number of multimodal deeplearning papers being published but also the increasing number of data sets and public challenges made available online
(see Table 2).

Medical applications
Deep learning in medical applications has become an important application domain that has attracted substantial interest. Medical imaging, for example, consists of a multitude

of multimodal data in the form of different medical imaging
modalities such as MRI, CT, positron emission tomography (PET), functional MRI (fMRI), X-ray, and ultrasound.
Although there have been notable improvements in new
medical imaging technologies, the interpretation of these
modalities for diagnosis still requires highly trained human
experts. Conventional computer vision approaches required
manually designed morphological feature representations.
However, transforming the tacit knowledge of human experts
into a computational form is not trivial. In the medical
imaging field, manually designing suitable image features
is extremely challenging, as it not only involves the interpretation of subtle visual markers and abnormalities, often
needing years of medical training, but also the need to fuse
complementary as well as possibly conflicting information
from multiple imaging modalities. Therefore, the ability to
learn these multimodal features through examples, as seen in
the success of deep learning applied to computer vision, has
prompted researchers to investigate their applicability in the
medical domain. It is therefore not surprising that an increasing amount of medical image analysis research in recent
years  [16], both unimodal as well as multimodal, involves
deep-learning-based methods.
Multimodal deep learning has been used in problems
involving tissue and organ segmentation  [17], multimodal
medical image retrieval [18], multimodal medical image registration [19], and computer-aided diagnosis [20]. A recent
review article by  Mamoshina et  al. [21] demonstrates the

IEEE SIGNAL PROCESSING MAGAZINE

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