A Review of Generalized Zero-Shot Learning Methods-Reference-Cited by-同舟云学术

A Review of Generalized Zero-Shot Learning Methods

Published:2022 Issue: Volume: Page:1-20
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Pourpanah Farhad¹^ORCID,Abdar Moloud²^ORCID,Luo Yuxuan³^ORCID,Zhou Xinlei⁴^ORCID,Wang Ran⁵^ORCID,Lim Chee Peng²^ORCID,Wang Xi-Zhao⁴^ORCID,Wu Q. M. Jonathan¹^ORCID

Affiliation:

1. Centre for Computer Vision and Deep Learning, Department of Electrical and Computer Engineering, University of Windsor, Windsor, Canada

2. Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia

3. Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China

4. College of Computer Science and Software Engineering, Guangdong Key Lab. of Intelligent Information Processing, Shenzhen University, Shenzhen, China

5. College of Mathematics and Statistics, Shenzhen Key Lab. of Advanced Machine Learning and Applications, Shenzhen University, Shenzhen, China

Funder

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

http://xplorestaging.ieee.org/ielx7/34/4359286/09832795.pdf?arnumber=9832795

Reference218 articles.

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3. ZEST: Zero-shot Learning from Text Descriptions using Textual Similarity and Visual Summarization

4. A Q-learning-based multi-agent system for data classification

5. Ridge regression, hubness, and zero-shot learning;shigeto;Proc Eur Conf Mach Learn Knowl Discovery Databases,2015

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