Visual Question Answering - From Theory to Application (Hardcover, 1st ed. 2022)

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.

General

Imprint:	Springer Nature
Country of origin:	Singapore
Series:	Advances in Computer Vision and Pattern Recognition
Release date:	May 2022
First published:	2022
Authors:	Qi Wu • Peng Wang • Xin Wang • Xiaodong He • Wenwu Zhu
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	238
Edition:	1st ed. 2022
ISBN-13:	978-981-19-0963-4
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Computing & IT > Applications of computing > Artificial intelligence > Knowledge-based systems / expert systems Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision
LSN:	981-19-0963-6
Barcode:	9789811909634

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