Computational knowledge vision: paradigmatic knowledge based prescriptive learning and reasoning for perception and vision
Zheng, Wenbo1; Yan, Lan2,3; Gou, Chao4; Wang, Fei-Yue2,3
Source PublicationARTIFICIAL INTELLIGENCE REVIEW
ISSN0269-2821
2022-03-21
Pages36
Corresponding AuthorWang, Fei-Yue(feiyue.wang@ia.ac.cn)
AbstractThis 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.
KeywordComputer vision Knowledge engineering Deep learning Graph learning Meta-learning Transformer Artificial intelligence (AI)
DOI10.1007/s10462-022-10166-9
WOS KeywordNEURAL-NETWORKS ; ARTIFICIAL-INTELLIGENCE ; SYSTEMS ; MODEL ; MULTIMEDIA ; OPTIMIZATION ; RECOGNITION ; PERSPECTIVE ; COMPLEX ; FUSION
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Key R&D Program of China ; Key Research and Development Program of Guangzhou ; National Natural Science Foundation of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000771386800002
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48100
Collection复杂系统管理与控制国家重点实验室_平行智能技术与系统团队
Corresponding AuthorWang, Fei-Yue
Affiliation1.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
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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