MuSe 2021 challenge : multimodal emotion, sentiment, physiological-emotion, and stress detection
Stappen, Lukas; Meßner, Eva-Maria; Cambria, Erik; Zhao, Guoying; Schuller, Björn W. (2021-10-21)
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
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https://urn.fi/URN:NBN:fi-fe2022020918401
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Abstract
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.
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