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
© 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.
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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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