Emotion Label

Emotion labeling in various modalities (text, speech, images, video) focuses on automatically identifying and classifying emotional states from data, aiming to improve human-computer interaction and related applications. Current research emphasizes multimodal fusion techniques, often employing transformer networks and contrastive learning to leverage information across different data types and address challenges like data imbalance and label ambiguity. This field is significant for advancing affective computing, enabling more nuanced and empathetic AI systems in areas such as mental health analysis, personalized education, and human-robot interaction.