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Visual question answering: a state-of-the-art review

Visual question answering: a state-of-the-art review Visual question answering (VQA) is a task that has received immense consideration from two major research communities: computer vision and natural language processing. Recently it has been widely accepted as an AI-complete task which can be used as an alternative to visual turing test. In its most common form, it is a multi-modal challenging task where a computer is required to provide the correct answer for a natural language question asked about an input image. It attracts many deep learning researchers after their remarkable achievements in text, voice and vision technologies. This review extensively and critically examines the current status of VQA research in terms of step by step solution methodologies, datasets and evaluation metrics. Finally, this paper also discusses future research directions for all the above-mentioned aspects of VQA separately. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

Visual question answering: a state-of-the-art review

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References (118)

Publisher
Springer Journals
Copyright
Copyright © Springer Nature B.V. 2020
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1007/s10462-020-09832-7
Publisher site
See Article on Publisher Site

Abstract

Visual question answering (VQA) is a task that has received immense consideration from two major research communities: computer vision and natural language processing. Recently it has been widely accepted as an AI-complete task which can be used as an alternative to visual turing test. In its most common form, it is a multi-modal challenging task where a computer is required to provide the correct answer for a natural language question asked about an input image. It attracts many deep learning researchers after their remarkable achievements in text, voice and vision technologies. This review extensively and critically examines the current status of VQA research in terms of step by step solution methodologies, datasets and evaluation metrics. Finally, this paper also discusses future research directions for all the above-mentioned aspects of VQA separately.

Journal

Artificial Intelligence ReviewSpringer Journals

Published: Dec 8, 2020

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