Reformulating Graph Kernels for Self-Supervised Space-Time Correspondence Learning-Reference-Cited by-同舟云学术

Reformulating Graph Kernels for Self-Supervised Space-Time Correspondence Learning

Author:

Qin Zheyun¹^ORCID,Lu Xiankai¹^ORCID,Liu Dongfang²^ORCID,Nie Xiushan³^ORCID,Yin Yilong¹^ORCID,Shen Jianbing⁴^ORCID,Loui Alexander C.²^ORCID

Affiliation:

1. School of Software, Shandong University, Jinan, China

2. Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, USA

3. School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China

4. Department of Computer and Information Science, State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China

Funder

National Natural Science Foundation of China

Major Basic Research Project of Shandong Natural Science Foundation

Natural Science Foundation of Shandong Province

Young Elite Scientists Sponsorship Program by the China Association for Science and Technology

Open Project of Key Laboratory of Artificial Intelligence, Ministry of Education;

Shandong Provincial Natural Science Foundation for Distinguished Young Scholars

Taishan Scholar Project of Shandong Province

FDCT

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Software

Link

Reference94 articles.

2. How powerful are graph neural networks?;xu;Proc Int Conf Learn Represent (ICLR),2019

3. Joint-task self-supervised learning for temporal correspondence;li;Proc Adv Neural Inf Process Syst (NeurIPS),2019

4. Weisfeiler-Lehman graph kernels;shervashidze;J Mach Learn Res,2011

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