Multi Modal Multi

Multimodal multitask learning aims to build artificial intelligence systems capable of processing diverse data types (e.g., text, images, audio) and performing multiple related tasks simultaneously. Current research focuses on developing unified model architectures, often based on large language models or transformers, that efficiently integrate different modalities and handle various tasks through techniques like multi-head attention, task-specific branches, and compact task representations (e.g., multimodal task vectors). This approach promises more efficient and generalizable AI systems, with applications ranging from medical dialogue summarization and cognitive load assessment to user interface modeling and body composition estimation.