MUSE Humor Sub Challenge

The MUSE Humor Sub-Challenge focuses on developing robust multimodal models for automatically detecting humor in audio-visual data, primarily leveraging datasets of spontaneous human interactions. Current research emphasizes hybrid multimodal fusion strategies, incorporating transformer networks, recurrent neural networks (like GRUs and LSTMs), and attention mechanisms to effectively integrate information from different modalities (audio, visual, and potentially text). Success in this area has significant implications for improving human-computer interaction, enabling more nuanced understanding of social cues in applications like virtual assistants and automated content analysis.