Meta-Generating Deep Attentive Metric for Few-Shot Classification-Reference-Cited by-同舟云学术

Meta-Generating Deep Attentive Metric for Few-Shot Classification

Published:2022-10 Issue:10 Volume:32 Page:6863-6873
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:

Zhou Fei¹,Zhang Lei²^ORCID,Wei Wei²^ORCID

Affiliation:

1. School of Computer Science, Northwestern Polytechnical University, Xi’an, China

2. Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, and the National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, School of Computer Science, Northwestern Polytechnical University, Xi’an, China

Funder

National Natural Science Foundation of China

Shenzhen Science and Technology Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

http://xplorestaging.ieee.org/ielx7/76/9910246/09771190.pdf?arnumber=9771190

Reference83 articles.

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2. MetaGAN: An adversarial approach to few-shot learning;zhang;Proc NeurIPS,2018

3. A simple neural attentive meta-learner;mishra;Proc ICLR,2018

4. Transductive propagation network for few-shot learning;liu;Proc Int Conf Learn Represent,2019

5. Adaptive Subspaces for Few-Shot Learning

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