Attention-Based Multi-View Feature Collaboration for Decoupled Few-Shot Learning-Reference-Cited by-同舟云学术

Attention-Based Multi-View Feature Collaboration for Decoupled Few-Shot Learning

Author:

Shao Shuai¹^ORCID,Xing Lei²^ORCID,Wang Yanjiang¹^ORCID,Liu Baodi¹^ORCID,Liu Weifeng¹^ORCID,Zhou Yicong³^ORCID

Affiliation:

1. College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China

2. College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, China

3. Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province, China

Fundamental Research Funds for the Central Universities, China University of Petroleum

Major Scientific and Technological Projects of CNPC

Creative Research Team of Young Scholars at Universities in Shandong Province

State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

Reference75 articles.

2. TapNet: Neural network augmented with task-adaptive projection for few-shot learning;yoon;Proc Int Conf Mach Learn,2019

4. Adaptive cross-modal few-shot learning;xing;Proc Adv Neural Inf Process Syst,2019

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