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001446836 001__ 1446836
001446836 003__ OCoLC
001446836 005__ 20230310004020.0
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001446836 019__ $$a1317834049$$a1318987609
001446836 020__ $$a9789811909641$$q(electronic bk.)
001446836 020__ $$a9811909644$$q(electronic bk.)
001446836 020__ $$z9789811909634$$q(print)
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001446836 0247_ $$a10.1007/978-981-19-0964-1$$2doi
001446836 035__ $$aSP(OCoLC)1319078410
001446836 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dN$T$$dOCLCF$$dUKAHL$$dOCLCQ
001446836 049__ $$aISEA
001446836 050_4 $$aTA1634
001446836 08204 $$a006.3/7$$223/eng/20220520
001446836 1001_ $$aWu, Qi,$$eauthor.$$0(orcid)0000-0003-3631-256X$$1https://orcid.org/0000-0003-3631-256X
001446836 24510 $$aVisual question answering :$$bfrom theory to application /$$cQi Wu, Peng Wang, Xin Wang, Xiaodong He, Wenwu Zhu.
001446836 264_1 $$aSingapore :$$bSpringer,$$c2022.
001446836 300__ $$a1 online resource (xiii, 238 pages) :$$billustrations (some color).
001446836 336__ $$atext$$btxt$$2rdacontent
001446836 337__ $$acomputer$$bc$$2rdamedia
001446836 338__ $$aonline resource$$bcr$$2rdacarrier
001446836 4901_ $$aAdvances in computer vision and pattern recognition,$$x2191-6594
001446836 504__ $$aIncludes bibliographical references and index.
001446836 5050_ $$a1. Introduction -- 2. Deep Learning Basics -- 3. Question Answering (QA) Basics -- 4. The Classical Visual Question Answering -- 5. Knowledge-based VQA.
001446836 506__ $$aAccess limited to authorized users.
001446836 520__ $$aVisual Question Answering (VQA) usually combines visual inputs like image and video with a natural language question concerning the input and generates a natural language answer as the output. This is by nature a multi-disciplinary research problem, involving computer vision (CV), natural language processing (NLP), knowledge representation and reasoning (KR), etc. Further, VQA is an ambitious undertaking, as it must overcome the challenges of general image understanding and the question-answering task, as well as the difficulties entailed by using large-scale databases with mixed-quality inputs. However, with the advent of deep learning (DL) and driven by the existence of advanced techniques in both CV and NLP and the availability of relevant large-scale datasets, we have recently seen enormous strides in VQA, with more systems and promising results emerging. This book provides a comprehensive overview of VQA, covering fundamental theories, models, datasets, and promising future directions. Given its scope, it can be used as a textbook on computer vision and natural language processing, especially for researchers and students in the area of visual question answering. It also highlights the key models used in VQA.
001446836 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed May 20, 2022).
001446836 650_0 $$aComputer vision.
001446836 650_0 $$aNatural language processing (Computer science)
001446836 650_0 $$aInformation visualization.
001446836 650_0 $$aMachine learning.
001446836 655_0 $$aElectronic books.
001446836 7001_ $$aWang, Peng,$$eauthor.$$0(orcid)0000-0001-7689-3405$$1https://orcid.org/0000-0001-7689-3405
001446836 7001_ $$aWang, Xin,$$eauthor.$$0(orcid)0000-0002-0351-2939$$1https://orcid.org/0000-0002-0351-2939
001446836 7001_ $$aHe, Xiaodong,$$d1973-$$eauthor.
001446836 7001_ $$aZhu, Wenwu,$$eauthor.
001446836 77608 $$iPrint version:$$z9811909636$$z9789811909634$$w(OCoLC)1295380275
001446836 830_0 $$aAdvances in computer vision and pattern recognition,$$x2191-6594
001446836 852__ $$bebk
001446836 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-0964-1$$zOnline Access$$91397441.1
001446836 909CO $$ooai:library.usi.edu:1446836$$pGLOBAL_SET
001446836 980__ $$aBIB
001446836 980__ $$aEBOOK
001446836 982__ $$aEbook
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