MuSe 2020 -- The First International Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop

• URL: http://arxiv.org/abs/2004.14858v3
• Date: Thu, 9 Jul 2020 08:37:43 GMT
• Title: MuSe 2020 -- The First International Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop
• Authors: Lukas Stappen, Alice Baird, Georgios Rizos, Panagiotis Tzirakis, Xinchen Du, Felix Hafner, Lea Schumann, Adria Mallol-Ragolta, Bj\"orn W. Schuller, Iulia Lefter, Erik Cambria, Ioannis Kompatsiaris
• Abstract summary: Multimodal Sentiment Analysis in Real-life Media (MuSe) 2020 is a Challenge-based Workshop focusing on the tasks of sentiment recognition. We present three distinct sub-challenges: MuSe-Wild, which focuses on continuous emotion prediction; MuSe-Topic, in which participants recognise domain-specific topics as the target of 3-class emotions; and MuSe-Trust, in which the novel aspect of trustworthiness is to be predicted.
• Score: 37.665121813745706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal Sentiment Analysis in Real-life Media (MuSe) 2020 is a Challenge-based Workshop focusing on the tasks of sentiment recognition, as well as emotion-target engagement and trustworthiness detection by means of more comprehensively integrating the audio-visual and language modalities. The purpose of MuSe 2020 is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), and the sentiment analysis community (symbol-based). We present three distinct sub-challenges: MuSe-Wild, which focuses on continuous emotion (arousal and valence) prediction; MuSe-Topic, in which participants recognise domain-specific topics as the target of 3-class (low, medium, high) emotions; and MuSe-Trust, in which the novel aspect of trustworthiness is to be predicted. In this paper, we provide detailed information on MuSe-CaR, the first of its kind in-the-wild database, which is utilised for the challenge, as well as the state-of-the-art features and modelling approaches applied. For each sub-challenge, a competitive baseline for participants is set; namely, on test we report for MuSe-Wild a combined (valence and arousal) CCC of .2568, for MuSe-Topic a score (computed as 0.34$\cdot$ UAR + 0.66$\cdot$F1) of 76.78 % on the 10-class topic and 40.64 % on the 3-class emotion prediction, and for MuSe-Trust a CCC of .4359.

Related papers

• The MuSe 2024 Multimodal Sentiment Analysis Challenge: Social Perception and Humor Recognition [64.5207572897806]
  The Multimodal Sentiment Analysis Challenge (MuSe) 2024 addresses two contemporary multimodal affect and sentiment analysis problems. In the Social Perception Sub-Challenge (MuSe-Perception), participants will predict 16 different social attributes of individuals. The Cross-Cultural Humor Detection Sub-Challenge (MuSe-Humor) dataset expands upon the Passau Spontaneous Football Coach Humor dataset.
  arXiv  Detail & Related papers  (2024-06-11T22:26:20Z)
• Exploiting Diverse Feature for Multimodal Sentiment Analysis [40.39627083212711]
  We present our solution to the MuSe-Personalisation sub-challenge in the MuSe 2023 Multimodal Sentiment Analysis Challenge. Considering different people have personal characteristics, the main challenge of this task is how to build robustness feature presentation for sentiment prediction.
  arXiv  Detail & Related papers  (2023-08-25T15:06:14Z)
• The MuSe 2023 Multimodal Sentiment Analysis Challenge: Mimicked Emotions, Cross-Cultural Humour, and Personalisation [69.13075715686622]
  MuSe 2023 is a set of shared tasks addressing three different contemporary multimodal affect and sentiment analysis problems. MuSe 2023 seeks to bring together a broad audience from different research communities.
  arXiv  Detail & Related papers  (2023-05-05T08:53:57Z)
• MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning [90.17500229142755]
  The first Multimodal Emotion Recognition Challenge (MER 2023) was successfully held at ACM Multimedia. This paper introduces the motivation behind this challenge, describe the benchmark dataset, and provide some statistics about participants. We believe this high-quality dataset can become a new benchmark in multimodal emotion recognition, especially for the Chinese research community.
  arXiv  Detail & Related papers  (2023-04-18T13:23:42Z)
• The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress [71.06453250061489]
  The Multimodal Sentiment Analysis Challenge (MuSe) 2022 is dedicated to multimodal sentiment and emotion recognition. For this year's challenge, we feature three datasets: (i) the Passau Spontaneous Football Coach Humor dataset that contains audio-visual recordings of German football coaches, labelled for the presence of humour; (ii) the Hume-Reaction dataset in which reactions of individuals to emotional stimuli have been annotated with respect to seven emotional expression intensities; and (iii) the Ulm-Trier Social Stress Test dataset comprising of audio-visual data labelled with continuous emotion values of people in stressful dispositions.
  arXiv  Detail & Related papers  (2022-06-23T13:34:33Z)
• The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress [42.475466121335636]
  Multimodal Sentiment Analysis (MuSe) 2021 is a challenge focusing on the tasks of sentiment and emotion. We present four distinct sub-challenges: MuSe-Wilder, MuSe-Stress, MuSe-Sent and MuSe-Physio. For each sub-challenge, a competitive baseline for participants is set; namely, on test, we report a Concordance Correlation Coefficient (CCC) of.4616 CCC for MuSe-Wilder;.4717 CCC for MuSe-Stress, and.4606 CCC for MuSe-Physio
  arXiv  Detail & Related papers  (2021-04-14T20:56:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.