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Visual question answeringfrom theory to application /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Visual question answeringby Qi Wu ... [et al.].
Reminder of title:	from theory to application /
other author:	Wu, Qi.
Published:	Singapore :Springer Nature Singapore :2022.
Description:	xiii, 238 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Computer vision.
Online resource:	https://doi.org/10.1007/978-981-19-0964-1
ISBN:	9789811909641$q(electronic bk.)

Visual question answeringfrom theory to application /
Visual question answeringfrom theory to application /[electronic resource] :by Qi Wu ... [et al.]. - Singapore :Springer Nature Singapore :2022. - xiii, 238 p. :ill. (some col.), digital ;24 cm. - Advances in computer vision and pattern recognition,2191-6594. - Advances in computer vision and pattern recognition..

1. Introduction -- 2. Deep Learning Basics -- 3. Question Answering (QA) Basics -- 4. The Classical Visual Question Answering -- 5. Knowledge-based VQA.

Visual Question Answering (VQA) usually combines visual inputs like image and video with a natural language question concerning the input and generates a natural language answer as the output. This is by nature a multi-disciplinary research problem, involving computer vision (CV), natural language processing (NLP), knowledge representation and reasoning (KR), etc. Further, VQA is an ambitious undertaking, as it must overcome the challenges of general image understanding and the question-answering task, as well as the difficulties entailed by using large-scale databases with mixed-quality inputs. However, with the advent of deep learning (DL) and driven by the existence of advanced techniques in both CV and NLP and the availability of relevant large-scale datasets, we have recently seen enormous strides in VQA, with more systems and promising results emerging. This book provides a comprehensive overview of VQA, covering fundamental theories, models, datasets, and promising future directions. Given its scope, it can be used as a textbook on computer vision and natural language processing, especially for researchers and students in the area of visual question answering. It also highlights the key models used in VQA.

ISBN: 9789811909641$q(electronic bk.)

Standard No.: 10.1007/978-981-19-0964-1doiSubjects--Topical Terms:

200113
Computer vision.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

Visual question answeringfrom theory to application /
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505 0 $a 1. Introduction -- 2. Deep Learning Basics -- 3. Question Answering (QA) Basics -- 4. The Classical Visual Question Answering -- 5. Knowledge-based VQA.
520 $a Visual Question Answering (VQA) usually combines visual inputs like image and video with a natural language question concerning the input and generates a natural language answer as the output. This is by nature a multi-disciplinary research problem, involving computer vision (CV), natural language processing (NLP), knowledge representation and reasoning (KR), etc. Further, VQA is an ambitious undertaking, as it must overcome the challenges of general image understanding and the question-answering task, as well as the difficulties entailed by using large-scale databases with mixed-quality inputs. However, with the advent of deep learning (DL) and driven by the existence of advanced techniques in both CV and NLP and the availability of relevant large-scale datasets, we have recently seen enormous strides in VQA, with more systems and promising results emerging. This book provides a comprehensive overview of VQA, covering fundamental theories, models, datasets, and promising future directions. Given its scope, it can be used as a textbook on computer vision and natural language processing, especially for researchers and students in the area of visual question answering. It also highlights the key models used in VQA.
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