國立虎尾科技大學 |

Visual Question Answering = From Theory to Application /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Visual Question Answering/ by Qi Wu, Peng Wang, Xin Wang, Xiaodong He, Wenwu Zhu.
其他題名:	From Theory to Application /
作者:	Wu, Qi.
其他作者:	Zhu, Wenwu.
面頁冊數:	XIII, 238 p. 104 illus., 92 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Logic in AI. -
電子資源:	https://doi.org/10.1007/978-981-19-0964-1
ISBN:	9789811909641

Visual Question Answering = From Theory to Application /
Wu, Qi.

Visual Question AnsweringFrom Theory to Application /[electronic resource] :by Qi Wu, Peng Wang, Xin Wang, Xiaodong He, Wenwu Zhu. - 1st ed. 2022. - XIII, 238 p. 104 illus., 92 illus. in color.online resource. - Advances in Computer Vision and Pattern Recognition,2191-6594. - Advances in Computer Vision and Pattern Recognition,.

1. Introduction -- 2. Deep Learning Basics -- 3. Question Answering (QA) Basics -- 4. The Classical Visual Question Answering -- 5. Knowledge-based VQA.

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.

ISBN: 9789811909641

Standard No.: 10.1007/978-981-19-0964-1doiSubjects--Topical Terms:

1228083
Logic in AI.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

Visual Question Answering = From Theory to Application /
LDR:02843nam a22004095i 4500 001 1087835
003 DE-He213
005 20220513040134.0
007 cr nn 008mamaa
008 221228s2022 si | s |||| 0|eng d
020 $a 9789811909641 $9 978-981-19-0964-1
024 7 $a 10.1007/978-981-19-0964-1 $2 doi
035 $a 978-981-19-0964-1
050 4 $a TA1634
072 7 $a UYQV $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYQV $2 thema
082 0 4 $a 006.37 $2 23
100 1 $a Wu, Qi. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1210149
245 1 0 $a Visual Question Answering $h [electronic resource] : $b From Theory to Application / $c by Qi Wu, Peng Wang, Xin Wang, Xiaodong He, Wenwu Zhu.
250 $a 1st ed. 2022.
264 1 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2022.
300 $a XIII, 238 p. 104 illus., 92 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Advances in Computer Vision and Pattern Recognition, $x 2191-6594
505 0 $a 1. Introduction -- 2. Deep Learning Basics -- 3. Question Answering (QA) Basics -- 4. The Classical Visual Question Answering -- 5. Knowledge-based VQA.
520 $a 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.
650 2 4 $a Logic in AI. $3 1228083
650 2 4 $a Knowledge Based Systems. $3 1365951
650 2 4 $a Machine Learning. $3 1137723
650 1 4 $a Computer Vision. $3 1127422
650 0 $a Logic programming. $3 670217
650 0 $a Expert systems (Computer science). $3 669964
650 0 $a Machine learning. $3 561253
650 0 $a Computer vision. $3 561800
700 1 $a Zhu, Wenwu. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1070378
700 1 $a He, Xiaodong. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1394924
700 1 $a Wang, Xin. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1019411
700 1 $a Wang, Peng. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1187591
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811909634
776 0 8 $i Printed edition: $z 9789811909658
776 0 8 $i Printed edition: $z 9789811909665
830 0 $a Advances in Computer Vision and Pattern Recognition, $x 2191-6586 $3 1256102
856 4 0 $u https://doi.org/10.1007/978-981-19-0964-1
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)