Multimodal Interaction

Multimodal interaction research focuses on developing systems that seamlessly integrate and interpret information from multiple sensory modalities (e.g., text, audio, vision) to enable more natural and effective human-computer interaction. Current research emphasizes developing robust model architectures, such as transformers and contrastive learning methods, to effectively fuse multimodal data and accurately infer user intent or emotion, often leveraging large language models for higher-level reasoning. This field is significant for advancing human-robot interaction, improving assistive technologies, and creating more intuitive interfaces for various applications, including autonomous driving and healthcare.