Signal Processing - November 2017 - 63
DEEP LEARNING FOR VISUAL UNDERSTANDING
Damien Teney, Qi Wu, and Anton van den Hengel
Visual Question Answering
A tutorial
T
he task of visual question answering (VQA) is receiving
increasing interest from researchers in both the computer
vision and natural language processing fields. Tremendous advances have been seen in the field of computer vision
due to the success of deep learning, in particular on low- and
midlevel tasks, such as image segmentation or object recognition. These advances have fueled researchers' confidence for
tackling more complex tasks that combine vision with language and high-level reasoning. VQA is a prime example of
this trend. This article presents the ongoing work in the field
and the current approaches to VQA based on deep learning.
VQA constitutes a test for deep visual understanding and a
benchmark for general artificial intelligence (AI). While the
field of VQA has seen recent successes, it remains a largely
unsolved task.
Introduction
©ISTOCKPHOTO.COM/ZAPP2PHOTO
Digital Object Identifier 10.1109/MSP.2017.2739826
Date of publication: 13 November 2017
1053-5888/17©2017IEEE
VQA involves an image and a related text question, to which
the machine must determine the correct answer. This task
spans the fields of computer vision and natural language
processing, since it requires both the comprehension of the
question and parsing the visual elements of the image. VQA
is a practical setting to evaluate deep visual understanding,
itself considered the overarching goal of the field of computer
vision. Deep visual understanding can be defined as the ability of algorithm to extract high-level information from images and to perform reasoning based on that information. In
this regard, VQA is an alternative to other tasks proposed to
evaluate this capability. Examples include the visual Turing
test [23], the task of image captioning [20], [73], and recent
works on visual dialogs [18].
A second parallel motivation for the study of VQA is its
utility in its own right. A system capable of answering questions about images has direct practical applications, such as
a personal assistant, or in robotics as aids for the visually
impaired. Note, however, that current VQA data sets do not
directly address this setting, because questions are typically
collected in a nongoal-oriented setting. Realistic, motivated
IEEE SIGNAL PROCESSING MAGAZINE
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November 2017
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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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