  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.
