9789811909665 - Visual Question Answering: From Theory to Application di Wu, Qi; Wang, Peng; Wang, Xin; He, Xiaodong; Zhu, Wenwu (11 risultati)

Condizione: New. In.

Condizione: New. pp. 252.

Condizione: New.

Paperback. Condizione: Brand New. 251 pages. 9.25x6.10x0.53 inches. In Stock.

Taschenbuch. Condizione: Neu. Visual Question Answering | From Theory to Application | Qi Wu (u. a.) | Taschenbuch | xiii | Englisch | 2023 | Springer | EAN 9789811909665 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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, andpromising 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.

Condizione: new. Questo è un articolo print on demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 252 pp. Englisch.

Condizione: New. PRINT ON DEMAND pp. 252.

Condizione: New. Print on Demand pp. 252.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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, andpromising 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 252 pp. Englisch.