Visual Question Answering: From Theory to Application

Visual Question Answering (VQA) is a multi-disciplinary research problem that combines visual inputs with natural language questions, requiring computer vision, natural language processing, knowledge representation, and reasoning. It has recently seen significant strides due to deep learning and the availability of large-scale datasets, with more systems and promising results emerging. This book provides a comprehensive overview of VQA, covering fundamental theories, models, datasets, and promising future directions, making it useful for researchers and students in the area of visual question answering.

Format: Paperback / softback
Length: 238 pages
Publication date: 15 May 2023
Publisher: Springer Verlag, Singapore

Visual Question Answering (VQA) is a multi-disciplinary research problem that combines visual inputs like images and videos with natural language questions to generate natural language answers. It involves computer vision (CV), natural language processing (NLP), knowledge representation and reasoning (KR), and other fields. VQA is challenging due to the complexity of general image understanding, question-answering, and the use of large-scale databases with mixed-quality inputs. However, recent advancements in deep learning (DL) have led to significant progress 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. 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.

Dimension: 235 x 155 (mm)
ISBN-13: 9789811909665
Edition number: 1st ed. 2022