Lukas Stappen, Eva-Maria Meßner, Erik Cambria, Guoying Zhao, and Björn W. Schuller. 2021. MuSe 2021 Challenge: Multimodal Emotion, Sentiment, Physiological-Emotion, and Stress Detection. Proceedings of the 29th ACM International Conference on Multimedia. Association for Computing Machinery, New York, NY, USA, 5706–5707. DOI:https://doi.org/10.1145/3474085.3478582
MuSe 2021 challenge : multimodal emotion, sentiment, physiological-emotion, and stress detection
|Author:||Stappen, Lukas1; Meßner, Eva-Maria2; Cambria, Erik3;|
1EIHW, University of Augsburg, Augsburg, Germany
2University of Ulm, Ulm, Germany
3Nanyang Technological University, Singapore
4University of Oulu, Oulu, Finland
5GLAM, Imperial College London, London, United Kingdom
|Online Access:||PDF Full Text (PDF, 0.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022020918401
Association for Computing Machinery,
|Publish Date:|| 2022-02-09
The 2nd Multimodal Sentiment Analysis (MuSe) 2021 Challenge-based Workshop is held in conjunction with ACM Multimedia’21. Two datasets are provided as part of the challenge. Firstly, the MuSe-CaR dataset, which focuses on user-generated, emotional vehicle reviews from YouTube, and secondly, the novel Ulm-Trier Social Stress (Ulm-TSST) dataset, which shows people in stressful circumstances. Participants are faced with four sub-challenges: predicting arousal and valence in a time- and value-continuous manner on a) MuSe-CaR (MuSe-Wilder) and b) Ulm-TSST (MuSe-Stress); c) predicting unsupervised created emotion classes on MuSe-CaR (MuSe-Sent); d) predicting a fusion of human-annotated arousal and measured galvanic skin response also as a continuous target on Ulm-TSST (MuSe-Physio). In this summary, we describe the motivation, the sub-challenges, the challenge conditions, the participation, and the most successful approaches.
|Pages:||5706 - 5707|
MM ’21: Proceedings of the 29th ACM International Conference on Multimedia, October 20–24, 2021 Virtual Event, China
ACM International Conference on Multimedia
|Field of Science:||
113 Computer and information sciences
1MuSe-CaR raw: https://zenodo.org/record/4651164; MuSe-Wilder: https://zenodo.org/record/4652376, MuSe-Sent: https://zenodo.org/record/4654371; Ulm-TSST raw: https://doi.org/10.5281/zenodo.4767117, MuSe-Stress: https://doi.org/10.5281/zenodo.4767114, MuSe-Physio: https://doi.org/10.5281/zenodo.4765992. https://github.com/lstappen/MuSe2021.
© 2021 Copyright held by the owner/author(s). This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 29th ACM International Conference on Multimedia, https://doi.org/10.1145/3474085.3478582.