소장자료
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| 020 | ▼a9789811909641▼9978-981-19-0964-1▲ | ||
| 024 | 7 | ▼a10.1007/978-981-19-0964-1▼2doi▲ | |
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| 100 | 1 | ▼aWu, Qi.▼eauthor.▼0(orcid)0000-0003-3631-256X▼1https://orcid.org/0000-0003-3631-256X▼4aut▼4http://id.loc.gov/vocabulary/relators/aut▲ | |
| 245 | 1 | 0 | ▼aVisual Question Answering▼h[electronic resource] :▼bFrom Theory to Application /▼cby Qi Wu, Peng Wang, Xin Wang, Xiaodong He, Wenwu Zhu.▲ |
| 250 | ▼a1st ed. 2022.▲ | ||
| 264 | 1 | ▼aSingapore :▼bSpringer Nature Singapore :▼bImprint: Springer,▼c2022.▲ | |
| 300 | ▼aXIII, 238 p. 104 illus., 92 illus. in color.▼bonline resource.▲ | ||
| 336 | ▼atext▼btxt▼2rdacontent▲ | ||
| 337 | ▼acomputer▼bc▼2rdamedia▲ | ||
| 338 | ▼aonline resource▼bcr▼2rdacarrier▲ | ||
| 347 | ▼atext file▼bPDF▼2rda▲ | ||
| 490 | 1 | ▼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.▲ | ||
| 650 | 0 | ▼aComputer vision.▲ | |
| 650 | 0 | ▼aMachine learning.▲ | |
| 650 | 0 | ▼aExpert systems (Computer science).▲ | |
| 650 | 0 | ▼aLogic programming.▲ | |
| 650 | 1 | 4 | ▼aComputer Vision.▲ |
| 650 | 2 | 4 | ▼aMachine Learning.▲ |
| 650 | 2 | 4 | ▼aKnowledge Based Systems.▲ |
| 650 | 2 | 4 | ▼aLogic in AI.▲ |
| 700 | 1 | ▼aWang, Peng.▼eauthor.▼0(orcid)0000-0001-7689-3405▼1https://orcid.org/0000-0001-7689-3405▼4aut▼4http://id.loc.gov/vocabulary/relators/aut▲ | |
| 700 | 1 | ▼aWang, Xin.▼eauthor.▼0(orcid)0000-0002-0351-2939▼1https://orcid.org/0000-0002-0351-2939▼4aut▼4http://id.loc.gov/vocabulary/relators/aut▲ | |
| 700 | 1 | ▼aHe, Xiaodong.▼eauthor.▼4aut▼4http://id.loc.gov/vocabulary/relators/aut▲ | |
| 700 | 1 | ▼aZhu, Wenwu.▼eauthor.▼4aut▼4http://id.loc.gov/vocabulary/relators/aut▲ | |
| 710 | 2 | ▼aSpringerLink (Online service)▲ | |
| 773 | 0 | ▼tSpringer Nature eBook▲ | |
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| 830 | 0 | ▼aAdvances in Computer Vision and Pattern Recognition,▼x2191-6594▲ | |
| 856 | 4 | 0 | ▼uhttps://doi.org/10.1007/978-981-19-0964-1▲ |
Visual Question Answering[electronic resource] : From Theory to Application
자료유형
국외eBook
서명/책임사항
Visual Question Answering [electronic resource] : From Theory to Application / by Qi Wu, Peng Wang, Xin Wang, Xiaodong He, Wenwu Zhu.
판사항
1st ed. 2022.
형태사항
XIII, 238 p. 104 illus., 92 illus. in color. online resource.
총서사항
내용주기
1. Introduction -- 2. Deep Learning Basics -- 3. Question Answering (QA) Basics -- 4. The Classical Visual Question Answering -- 5. Knowledge-based VQA.
요약주기
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.
주제
ISBN
9789811909641
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