Does Hard-Negative Contrastive Learning Improve Facial Emotion Recognition?-Reference-Cited by-同舟云学术

Does Hard-Negative Contrastive Learning Improve Facial Emotion Recognition?

Published:2024-03-12 Issue: Volume:11 Page:162-168
ISSN:
Container-title:Proceedings of the 2024 7th International Conference on Machine Vision and Applications
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Author:

Win Khin Cho¹^ORCID,Akhtar Zahid²^ORCID,Mohan C. Krishna³^ORCID

Affiliation:

1. Department of Artificial Intelligence, Indian Institute of Information Technology, Hyderabad (IIT Hyderabad), India

2. Department of Network and Computer Security, State University of New York Polytechnic Institute (USA), USA

3. Department of Computer Science and Engineering, Indian Institute of Information Technology, Hyderabad (IIT Hyderabad), India

Funder

the Doctoral fellowship in India for ASEAN (DIA) programme, the Ministry of Education, Government of India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3653946.3653971

Reference48 articles.

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3. Jia-Ren Chang, Yong-Sheng Chen, and Wei-Chen Chiu. 2021. Learning facial representations from the cycle-consistency of face. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 9680–9689.

4. Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597–1607.

5. Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton. 2020. Big Self-Supervised Models are Strong Semi-Supervised Learners. NeurIPS (2020), 22243–22255.