Mining knowledge graphs for vision tasks

Xiaojun Chang; Fengda Zhu; Xiaoran Bi; Weili Guan; Zongyuan Ge; Minnan Luo

doi:10.1007/978-3-030-18590-9b

Mining knowledge graphs for vision tasks

Xiaojun Chang
, Fengda Zhu
, Xiaoran Bi
, Weili Guan
, Zongyuan Ge
, Minnan Luo

Faculty of Electronic and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Semantic technologies, such as knowledge graph, have been of great interest to the community of different areas. Recent advances in knowledge acquisition, alignment, and utilization have resulted in a bunch of new approaches for knowledge graph learning in computer vision tasks. This tutorial focuses on the end-to-end utilization of knowledge graph in computer vision tasks.

Original language	English
Title of host publication	Database Systems for Advanced Applications - DASFAA 2019 International Workshops
Subtitle of host publication	BDMS, BDQM, and GDMA, Proceedings
Editors	Yongxin Tong, Jun Yang, Guoliang Li, Juggapong Natwichai, Joao Gama
Publisher	Springer Verlag
Pages	592-594
Number of pages	3
ISBN (Print)	9783030185893
DOIs	https://doi.org/10.1007/978-3-030-18590-9b
State	Published - 2019
Event	24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 - Chiang Mai, Thailand Duration: 22 Apr 2019 → 25 Apr 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11448 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on Database Systems for Advanced Applications, DASFAA 2019
Country/Territory	Thailand
City	Chiang Mai
Period	22/04/19 → 25/04/19

Keywords

Computer vision
Knowledge graph
Visual reasoning
Zero-shot learning

Access to Document

10.1007/978-3-030-18590-9b

Cite this

Chang, X., Zhu, F., Bi, X., Guan, W., Ge, Z., & Luo, M. (2019). Mining knowledge graphs for vision tasks. In Y. Tong, J. Yang, G. Li, J. Natwichai, & J. Gama (Eds.), Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings (pp. 592-594). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11448 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-18590-9b

Chang, Xiaojun ; Zhu, Fengda ; Bi, Xiaoran et al. / Mining knowledge graphs for vision tasks. Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. editor / Yongxin Tong ; Jun Yang ; Guoliang Li ; Juggapong Natwichai ; Joao Gama. Springer Verlag, 2019. pp. 592-594 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{9500c821aabb41d1a51370f6b31edecf,

title = "Mining knowledge graphs for vision tasks",

abstract = "Semantic technologies, such as knowledge graph, have been of great interest to the community of different areas. Recent advances in knowledge acquisition, alignment, and utilization have resulted in a bunch of new approaches for knowledge graph learning in computer vision tasks. This tutorial focuses on the end-to-end utilization of knowledge graph in computer vision tasks.",

keywords = "Computer vision, Knowledge graph, Visual reasoning, Zero-shot learning",

author = "Xiaojun Chang and Fengda Zhu and Xiaoran Bi and Weili Guan and Zongyuan Ge and Minnan Luo",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 ; Conference date: 22-04-2019 Through 25-04-2019",

year = "2019",

doi = "10.1007/978-3-030-18590-9b",

language = "英语",

isbn = "9783030185893",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "592--594",

editor = "Yongxin Tong and Jun Yang and Guoliang Li and Juggapong Natwichai and Joao Gama",

booktitle = "Database Systems for Advanced Applications - DASFAA 2019 International Workshops",

}

Chang, X, Zhu, F, Bi, X, Guan, W, Ge, Z & Luo, M 2019, Mining knowledge graphs for vision tasks. in Y Tong, J Yang, G Li, J Natwichai & J Gama (eds), Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11448 LNCS, Springer Verlag, pp. 592-594, 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, Chiang Mai, Thailand, 22/04/19. https://doi.org/10.1007/978-3-030-18590-9b

Mining knowledge graphs for vision tasks. / Chang, Xiaojun; Zhu, Fengda; Bi, Xiaoran et al.
Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. ed. / Yongxin Tong; Jun Yang; Guoliang Li; Juggapong Natwichai; Joao Gama. Springer Verlag, 2019. p. 592-594 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11448 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Mining knowledge graphs for vision tasks

AU - Chang, Xiaojun

AU - Zhu, Fengda

AU - Bi, Xiaoran

AU - Guan, Weili

AU - Ge, Zongyuan

AU - Luo, Minnan

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - Semantic technologies, such as knowledge graph, have been of great interest to the community of different areas. Recent advances in knowledge acquisition, alignment, and utilization have resulted in a bunch of new approaches for knowledge graph learning in computer vision tasks. This tutorial focuses on the end-to-end utilization of knowledge graph in computer vision tasks.

AB - Semantic technologies, such as knowledge graph, have been of great interest to the community of different areas. Recent advances in knowledge acquisition, alignment, and utilization have resulted in a bunch of new approaches for knowledge graph learning in computer vision tasks. This tutorial focuses on the end-to-end utilization of knowledge graph in computer vision tasks.

KW - Computer vision

KW - Knowledge graph

KW - Visual reasoning

KW - Zero-shot learning

UR - https://www.scopus.com/pages/publications/85065424980

U2 - 10.1007/978-3-030-18590-9b

DO - 10.1007/978-3-030-18590-9b

M3 - 会议稿件

AN - SCOPUS:85065424980

SN - 9783030185893

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 592

EP - 594

BT - Database Systems for Advanced Applications - DASFAA 2019 International Workshops

A2 - Tong, Yongxin

A2 - Yang, Jun

A2 - Li, Guoliang

A2 - Natwichai, Juggapong

A2 - Gama, Joao

PB - Springer Verlag

T2 - 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019

Y2 - 22 April 2019 through 25 April 2019

ER -

Chang X, Zhu F, Bi X, Guan W, Ge Z, Luo M. Mining knowledge graphs for vision tasks. In Tong Y, Yang J, Li G, Natwichai J, Gama J, editors, Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. Springer Verlag. 2019. p. 592-594. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-18590-9b

Mining knowledge graphs for vision tasks

Abstract

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Keywords

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