Multimodal Network

Multimodal networks integrate information from diverse data sources (e.g., text, images, audio) to improve performance on complex tasks compared to single-modality approaches. Current research emphasizes developing robust architectures, such as those employing transformer networks, that handle missing modalities and efficiently fuse information from different sources, including through techniques like early and late fusion, and dynamic fusion strategies. This field is significant for advancing artificial intelligence, particularly in applications like emotion recognition, action recognition, and medical diagnosis, where integrating multiple data types can lead to more accurate and reliable results.