Supervised Contrastive Learning for Affect Modelling

Author:

Pinitas Kosmas1ORCID,Makantasis Konstantinos1ORCID,Liapis Antonios1ORCID,Yannakakis Georgios N.1ORCID

Affiliation:

1. Institute of Digital Games, University of Malta, Malta

Funder

H2020 Industrial Leadership

H2020 Marie Skodowska-Curie Actions

Publisher

ACM

Reference51 articles.

1. Babak Joze Abbaschian , Daniel Sierra-Sosa , and Adel Elmaghraby . 2021. Deep learning techniques for speech emotion recognition, from databases to models. Sensors 21, 4 ( 2021 ). Babak Joze Abbaschian, Daniel Sierra-Sosa, and Adel Elmaghraby. 2021. Deep learning techniques for speech emotion recognition, from databases to models. Sensors 21, 4 (2021).

2. Multimodal Emotion Recognition using Deep Learning

3. Deep learning vs. kernel methods: Performance for emotion prediction in videos

4. Yonatan Belinkov . 2021. Probing classifiers: Promises, shortcomings, and alternatives. arXiv e-prints ( 2021 ), arXiv–2102. Yonatan Belinkov. 2021. Probing classifiers: Promises, shortcomings, and alternatives. arXiv e-prints (2021), arXiv–2102.

5. Margaret  M Bradley and Peter  J Lang . 2000. Measuring emotion: Behavior, feeling, and physiology . In Cognitive neuroscience of emotion . Oxford University Press , 242–276. Margaret M Bradley and Peter J Lang. 2000. Measuring emotion: Behavior, feeling, and physiology. In Cognitive neuroscience of emotion. Oxford University Press, 242–276.

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