Towards Bridged Vision and Language: Learning Cross-Modal Knowledge Representation for Relation Extraction-Reference-Cited by-同舟云学术

Towards Bridged Vision and Language: Learning Cross-Modal Knowledge Representation for Relation Extraction

Published:2024-01 Issue:1 Volume:34 Page:561-575
ISSN:1051-8215
Container-title:IEEE Transactions on Circuits and Systems for Video Technology
language:
Short-container-title:IEEE Trans. Circuits Syst. Video Technol.

Author:

Feng Junhao¹^ORCID,Wang Guohua¹^ORCID,Zheng Changmeng²^ORCID,Cai Yi¹^ORCID,Fu Ze¹^ORCID,Wang Yaowei³^ORCID,Wei Xiao-Yong⁴^ORCID,Li Qing²^ORCID

Affiliation:

1. School of Software Engineering, and the Key Laboratory of Big Data and Intelligent Robot, Ministry of Education, South China University of Technology (SCUT), Guangzhou, China

2. Department of Computing, The Hong Kong Polytechnic University, Jurong West, Hong Kong

3. Peng Cheng Laboratory, Shenzhen, China

4. School of Computer Science, Sichuan University, Chengdu, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities, South China University of Technology

Science and Technology Planning Project of Guangdong Province

Chinese Association for Artificial Intelligence (CAAI)-Huawei MindSpore Open Fund

China Computer Federation (CCF)-Zhipu AI Large Model Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

http://xplorestaging.ieee.org/ielx7/76/10382161/10147282.pdf?arnumber=10147282

Reference55 articles.

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2. FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

3. SemEval-2010 task 8

4. The automatic content extraction (ACE) program-tasks, data, and evaluation;Doddington

5. Relation classification via convolutional deep neural network;Zeng