Modality Interaction

Modality interaction research focuses on effectively combining information from different data sources (e.g., images, text, audio) to improve the performance of machine learning models. Current efforts concentrate on developing sophisticated architectures, such as those employing attention mechanisms, graph neural networks, and diffusion models, to learn both intra-modal (within a single modality) and inter-modal (between modalities) relationships. This field is crucial for advancing applications across diverse domains, including image-text retrieval, emotion recognition, and medical diagnosis, where integrating multiple data types can lead to more accurate and robust systems. The development of efficient and effective modality interaction techniques is driving progress in many areas of artificial intelligence.