Adaptive Global-Local Representation Learning and Selection for Cross-Domain Facial Expression Recognition-Reference-Cited by-同舟云学术

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Adaptive Global-Local Representation Learning and Selection for Cross-Domain Facial Expression Recognition

Published:2024 Issue: Volume:26 Page:6676-6688
ISSN:1520-9210
Container-title:IEEE Transactions on Multimedia
language:
Short-container-title:IEEE Trans. Multimedia

Author:

Gao Yuefang¹^ORCID,Xie Yuhao²^ORCID,Hu Zeke Zexi³^ORCID,Chen Tianshui⁴^ORCID,Lin Liang⁵^ORCID

Affiliation:

1. South China Agricultural University, Guangzhou, China

2. Guangzhou Institute of Technology, Xidian University, Guangzhou, China

3. School of Computer Science, University of Sydney, Darlington, NSW, Australia

4. Guangdong University of Technology, Guangzhou, China

5. Sun Yat-Sen University, Guangzhou, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Guangzhou Basic and Applied Basic Research Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6046/10384483/10404024.pdf?arnumber=10404024

Reference63 articles.

1. Domain Invariant Feature Learning for Speaker-Independent Speech Emotion Recognition

2. Cross-Database Micro-Expression Recognition: A Benchmark

3. Cross-Dataset Transfer Driver Expression Recognition via Global Discriminative and Local Structure Knowledge Exploitation in Shared Projection Subspace

4. Dynamic Objectives Learning for Facial Expression Recognition

5. Identity-aware Facial Expression Recognition via Deep Metric Learning based on Synthesized Images

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