@inproceedings{99b9ba1e2a2641159b6d869f52f99a8a,
title = "MuSe 2021 Challenge: Multimodal Emotion, Sentiment, Physiological-Emotion, and Stress Detection",
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.",
keywords = "affective computing, challenge, emotion recognition, multimodal sentiment analysis, muse, stress recognition",
author = "Lukas Stappen and Me{\ss}ner, {Eva Maria} and Erik Cambria and Guoying Zhao and Schuller, {Bj{\"o}rn W.}",
note = "Publisher Copyright: {\textcopyright} 2021 Owner/Author.; 29th ACM International Conference on Multimedia, MM 2021 ; Conference date: 20-10-2021 Through 24-10-2021",
year = "2021",
month = oct,
day = "17",
doi = "10.1145/3474085.3478582",
language = "English",
series = "MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia",
publisher = "Association for Computing Machinery, Inc",
pages = "5706--5707",
booktitle = "MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia",
}