Sub Challenge

Sub-challenges in affective computing focus on automatically recognizing and classifying various emotional states from multimodal data (audio, video, text). Current research emphasizes the use of deep learning models, such as recurrent neural networks (RNNs) and transformers, often incorporating multimodal fusion techniques to improve accuracy in tasks like emotion detection, humor recognition, and stress level assessment. These challenges drive advancements in artificial intelligence and have significant implications for mental health diagnostics, personalized healthcare, and human-computer interaction.