CLC number:
On-line Access: 2021-05-17
Received: 2022-04-22
Revision Accepted: 2022-04-22
Crosschecked: 0000-00-00
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Yun-he Pan. Miniaturized five fundamental issues about visual knowledge[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(5): 615-618.
@article{title="Miniaturized five fundamental issues about visual knowledge",
author="Yun-he Pan",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="5",
pages="615-618",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2040000"
}
%0 Journal Article
%T Miniaturized five fundamental issues about visual knowledge
%A Yun-he Pan
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 5
%P 615-618
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2040000
TY - JOUR
T1 - Miniaturized five fundamental issues about visual knowledge
A1 - Yun-he Pan
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 5
SP - 615
EP - 618
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2040000
Abstract: The five fundamental issues on visual knowledge are expression of visual knowledge, visual recognition, simulation of visual imagery thinking, learning of visual knowledge, and multiple expressions of knowledge. Our analysis shows that the distinct advantages of visual knowledge are its capacity to generate comprehensive imagery, its spatio-temporal evolution capacity and imagery display capacity. These are the features currently lacking in character knowledge and DNN. Integration of AI and CAD/CG/CV technologies will provide a vital foundation for the development of AI in terms of creation, prediction, and man-machine integration. The study of visual knowledge and multiple expressions of knowledge is the key to the development of visual intelligence and the main theory and technology to enable AI 2.0 to make major breakthroughs. It is a desolate, clammy, and fertile “Great Northern Wilderness,” but also a “depopulated land” full of hope and worthy of multi-disciplinary cooperation.
[1]Brown TB, Mann B, Ryder N, et al., 2020. Language models are few-shot learners. https://arxiv.org/abs/2005.14165
[2]Pan YH, 2016. Heading toward artificial intelligence 2.0. Engineering, 2(4):409-413.
[3]Pan YH, 2019. On visual knowledge. Front Inform Technol Electron Eng, 20(8):1021-1025.
[4]Pan YH, 2020. Multiple knowledge representation of artificial intelligence. Engineering, 6(3):216-217.
[5]Tang SL, Zhang Q, Zheng TP, et al., 2018. Two step joint model for drug drug interaction extraction. https://arxiv.org/abs/2008.12704
[6]Xu DF, Zhu YK, Choy CB, et al., 2017. Scene graph generation by iterative message passing. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.5410-5419.
[7]Zellers R, Yatskar M, Thomson S, et al., 2018. Neural motifs: scene graph parsing with global context. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.5831-5840.
[8]Zhang NY, Deng SM, Zhang W, et al., 2020. Relation adversarial network for low resource knowledge graph completion. Proc Web Conf, p.1-12.
[9]Zhang SY, Tan ZQ, Zhou Z, et al., 2020. Comprehensive in-formation integration modeling framework for video titling. Proc SIGKDD Int Conf on Knowledge Discovery & Data Mining, p.2744-2754.
[10]Zhuang YT, Jain R, Gao W, et al., 2017. Panel: cross-media intelligence. Proc 25th ACM Int Conf on Multimedia, p.1173.
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