TY  - BOOK
AU  - Wu,Qi
AU  - Wang,Peng
AU  - Wang,Xin
AU  - He,Xiaodong
AU  - Zhu,Wenwu
TI  - Visual Question Answering: From Theory to Application 
T2  - Advances in Computer Vision and Pattern Recognition,
SN  - 9789811909641
U1  - 006.37 23
PY  - 2022///
CY  - Singapore
PB  - Springer Nature Singapore, Imprint: Springer
KW  - Computer vision
KW  - Machine learning
KW  - Expert systems (Computer science)
KW  - Logic programming
KW  - Computer Vision
KW  - Machine Learning
KW  - Knowledge Based Systems
KW  - Logic in AI
N1  - 1. Introduction -- 2. Deep Learning Basics -- 3. Question Answering (QA) Basics  -- 4. The Classical Visual Question Answering -- 5. Knowledge-based VQA
N2  - 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
UR  - https://doi.org/10.1007/978-981-19-0964-1
ER  -