Multimodal Learning Method

Multimodal learning aims to improve machine learning performance by integrating data from multiple sources (e.g., images, text, physiological signals), leveraging the complementary information each modality provides. Current research emphasizes robust methods handling incomplete or heterogeneous data, employing architectures like attention mechanisms, graph convolutional networks, and transformers to effectively fuse information across modalities. This approach is proving valuable in diverse applications, including medical diagnosis, vehicle design, and fake video detection, by enabling more accurate and comprehensive analyses than unimodal methods.