The MuSe 2024 Multimodal Sentiment Analysis Challenge: Social Perception and Humor Recognition

• URL: http://arxiv.org/abs/2406.07753v1
• Date: Tue, 11 Jun 2024 22:26:20 GMT
• Title: The MuSe 2024 Multimodal Sentiment Analysis Challenge: Social Perception and Humor Recognition
• Authors: Shahin Amiriparian, Lukas Christ, Alexander Kathan, Maurice Gerczuk, Niklas Müller, Steffen Klug, Lukas Stappen, Andreas König, Erik Cambria, Björn Schuller, Simone Eulitz,
• Abstract summary: 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.
• Score: 64.5207572897806
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 such as assertiveness, dominance, likability, and sincerity based on the provided audio-visual data. The Cross-Cultural Humor Detection Sub-Challenge (MuSe-Humor) dataset expands upon the Passau Spontaneous Football Coach Humor (Passau-SFCH) dataset, focusing on the detection of spontaneous humor in a cross-lingual and cross-cultural setting. The main objective of MuSe 2024 is to unite a broad audience from various research domains, including multimodal sentiment analysis, audio-visual affective computing, continuous signal processing, and natural language processing. By fostering collaboration and exchange among experts in these fields, the MuSe 2024 endeavors to advance the understanding and application of sentiment analysis and affective computing across multiple modalities. This baseline paper provides details on each sub-challenge and its corresponding dataset, extracted features from each data modality, and discusses challenge baselines. For our baseline system, we make use of a range of Transformers and expert-designed features and train Gated Recurrent Unit (GRU)-Recurrent Neural Network (RNN) models on them, resulting in a competitive baseline system. On the unseen test datasets of the respective sub-challenges, it achieves a mean Pearson's Correlation Coefficient ($\rho$) of 0.3573 for MuSe-Perception and an Area Under the Curve (AUC) value of 0.8682 for MuSe-Humor.

Related papers

• 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)
• Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results [84.37263300062597]
  Humor is a substantial element of human social behavior, affect, and cognition. Current methods of humor detection have been exclusively based on staged data, making them inadequate for "real-world" applications. We contribute to addressing this deficiency by introducing the novel Passau-Spontaneous Football Coach Humor dataset, comprising about 11 hours of recordings.
  arXiv  Detail & Related papers  (2022-09-28T17:36:47Z)
• Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysis [31.097398034974436]
  We present our solutions for the Multimodal Sentiment Analysis Challenge (MuSe) 2022, which includes MuSe-Humor, MuSe-Reaction and MuSe-Stress Sub-challenges. The MuSe 2022 focuses on humor detection, emotional reactions and multimodal emotional stress utilising different modalities and data sets.
  arXiv  Detail & Related papers  (2022-08-05T09:07:58Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• 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 Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements [14.707930573950787]
  We present MuSe-CaR, a first of its kind multimodal dataset. The data is publicly available as it recently served as the testing bed for the 1st Multimodal Sentiment Analysis Challenge.
  arXiv  Detail & Related papers  (2021-01-15T10:40:37Z)

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.