MuSe 2021 Challenge

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.

Stappen Lukas, Meßner Eva-Maria, Cambria Erik, Zhao Guoying, Schuller Björn W.

MM ’21: Proceedings of the 29th ACM International Conference on Multimedia, October 20–24, 2021 Virtual Event, China

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: