Modality Model

Modality models aim to integrate information from multiple data sources (modalities), such as images, text, and sensor readings, to create richer and more comprehensive representations than single-modality approaches allow. Current research focuses on developing effective architectures, often employing contrastive learning and masked autoencoders, to handle the challenges of modality disparities and limited labeled data, particularly within federated learning settings where data is distributed across multiple devices. These advancements hold significant promise for improving performance in diverse applications, including medical image analysis (e.g., brain tumor segmentation, ECG interpretation) and multimodal understanding tasks, by leveraging the synergistic power of combined data streams.