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020			_a9789811909641
024	7		_a10.1007/978-981-19-0964-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1634 _b2022 EB
100	1		_aWu, Qi _eautor _0(orcid)0000-0003-3631-256X _1https://orcid.org/0000-0003-3631-256X _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685208
245	1	0	_aVisual Question Answering : _bfrom Theory to Application _cby Qi Wu, Peng Wang, Xin Wang, Xiaodong He, Wenwu Zhu
250			_aFirst edition 2022
264		1	_aSingapore _bSpringer International Publising _c2022
300			_a1 recurso en línea (XIII, 238 páginas) _b104 ilustraciones, 92 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aAdvances in Computer Vision and Pattern Recognition _x2191-6594
505	0		_a1. Introduction -- 2. Deep Learning Basics -- 3. Question Answering (QA) Basics -- 4. The Classical Visual Question Answering -- 5. Knowledge-based VQA.
520			_aVisual 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.
988			_aSpringer_Computer_2022
650		7	_2embne _9159793 _aVisión por ordenador
650		7	_2embne _9158738 _aProceso en lenguaje natural (Informática)
700	1		_aWang, Peng _eautor _0(orcid)0000-0001-7689-3405 _1https://orcid.org/0000-0001-7689-3405 _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685209
700	1		_aWang, Xin _eautor _0(orcid)0000-0002-0351-2939 _1https://orcid.org/0000-0002-0351-2939 _4aut _4http://id.loc.gov/vocabulary/relators/aut _9681443
700	1		_aHe, Xiaodong, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685210 _d1973-
700	1		_aZhu, Wenwu _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _999421
776	0	8	_iPrinted edition: _z9789811909634
776	0	8	_iPrinted edition: _z9789811909658
776	0	8	_iPrinted edition: _z9789811909665
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-0964-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2022 _dz _eIG _zSI