國立高雄大學圖資館 |

登入

回首頁

Visual question answeringfrom theory to application /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Visual question answeringby Qi Wu ... [et al.].
其他題名:	from theory to application /
其他作者:	Wu, Qi.
出版者:	Singapore :Springer Nature Singapore :2022.
面頁冊數:	xiii, 238 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Computer vision.
電子資源:	https://doi.org/10.1007/978-981-19-0964-1
ISBN:	9789811909641$q(electronic bk.)

Visual question answeringfrom theory to application /
Visual question answeringfrom theory to application /[electronic resource] :by Qi Wu ... [et al.]. - Singapore :Springer Nature Singapore :2022. - xiii, 238 p. :ill. (some col.), digital ;24 cm. - 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$q(electronic bk.)

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

200113
Computer vision.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

Visual question answeringfrom theory to application /
LDR:02449nmm a2200337 a 4500 001 620654
003 DE-He213
005 20220513040134.0
006 m d
007 cr nn 008maaau
008 221121s2022 si s 0 eng d
020 $a 9789811909641$q(electronic bk.)
020 $a 9789811909634$q(paper)
024 7 $a 10.1007/978-981-19-0964-1 $2 doi
035 $a 978-981-19-0964-1
040 $a GP $c GP
041 0 $a eng
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
090 $a TA1634 $b .V834 2022
245 0 0 $a Visual question answering $h [electronic resource] : $b from theory to application / $c by Qi Wu ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2022.
300 $a xiii, 238 p. : $b ill. (some col.), digital ; $c 24 cm.
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 0 $a Computer vision. $3 200113
650 0 $a Natural language processing (Computer science) $3 200539
650 0 $a Information visualization. $3 248886
650 0 $a Machine learning. $3 188639
650 1 4 $a Computer Vision. $3 913138
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Knowledge Based Systems. $3 913337
650 2 4 $a Logic in AI. $3 836108
700 1 $a Wu, Qi. $3 824523
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Advances in computer vision and pattern recognition. $3 559645
856 4 0 $u https://doi.org/10.1007/978-981-19-0964-1
950 $a Computer Science (SpringerNature-11645)