Computational knowledge vision: paradigmatic knowledge based prescriptive learning and reasoning for perception and vision

doi:10.1007/s10462-022-10166-9

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Computational knowledge vision: paradigmatic knowledge based prescriptive learning and reasoning for perception and vision
	Zheng, Wenbo 1; Yan, Lan 2,3; Gou, Chao4 ; Wang, Fei-Yue2,3
发表期刊	ARTIFICIAL INTELLIGENCE REVIEW
ISSN	0269-2821
	2022-03-21
页码	36
通讯作者	Wang, Fei-Yue(feiyue.wang@ia.ac.cn)
摘要	This paper outlines a novel advanced framework that combines structurized knowledge and visual models-Computational Knowledge Vision. In advanced studies of image and visual perception, a visual model's understanding and reasoning ability often determines whether it works well in complex scenarios. This paper presents the state-of-the-art mainstream of vision models for visual perception. This paper then proposes a concept and basic framework of Computational Knowledge Vision that extends the knowledge engineering methodology to the computer vision field. In this paper, we first retrospect prior work related to Computational Knowledge Vision in the light of the connectionist and symbolist streams. We discuss neural network models, meta-learning models, graph models, and Transformer models in detail. We then illustrate a basic framework for Computational Knowledge Vision, whose essential techniques include structurized knowledge, knowledge projection, and conditional feedback. The goal of the framework is to enable visual models to gain the ability of representation, understanding, and reasoning. We also describe in-depth works in Computational Knowledge Vision and its extensions in other fields.
关键词	Computer vision Knowledge engineering Deep learning Graph learning Meta-learning Transformer Artificial intelligence (AI)
DOI	10.1007/s10462-022-10166-9
关键词[WOS]	NEURAL-NETWORKS ; ARTIFICIAL-INTELLIGENCE ; SYSTEMS ; MODEL ; MULTIMEDIA ; OPTIMIZATION ; RECOGNITION ; PERSPECTIVE ; COMPLEX ; FUSION
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2018AAA0101502] ; Key Research and Development Program of Guangzhou[202007050002] ; National Natural Science Foundation of China[61806198] ; National Natural Science Foundation of China[U1811463]
项目资助者	National Key R&D Program of China ; Key Research and Development Program of Guangzhou ; National Natural Science Foundation of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000771386800002
出版者	SPRINGER
七大方向——子方向分类	知识表示与推理
引用统计	被引频次：11[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48100
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Wang, Fei-Yue
作者单位	1.Wuhan Univ Technol, Sch Comp & Artificial Intelligence, Wuhan, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 4.Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zheng, Wenbo,Yan, Lan,Gou, Chao,et al. Computational knowledge vision: paradigmatic knowledge based prescriptive learning and reasoning for perception and vision[J]. ARTIFICIAL INTELLIGENCE REVIEW,2022:36.
APA	Zheng, Wenbo,Yan, Lan,Gou, Chao,&Wang, Fei-Yue.(2022).Computational knowledge vision: paradigmatic knowledge based prescriptive learning and reasoning for perception and vision.ARTIFICIAL INTELLIGENCE REVIEW,36.
MLA	Zheng, Wenbo,et al."Computational knowledge vision: paradigmatic knowledge based prescriptive learning and reasoning for perception and vision".ARTIFICIAL INTELLIGENCE REVIEW (2022):36.