Multimodal in Context Learning

Multimodal in-context learning (M-ICL) explores how large multimodal models (LMMs) can learn new tasks from a few examples without retraining, leveraging diverse data modalities like text and images. Current research focuses on understanding the mechanisms of M-ICL, improving its efficiency through techniques like multimodal task vectors and context-aware modules, and developing better datasets and evaluation benchmarks for diverse tasks. This field is significant because it promises more efficient and adaptable AI systems, with applications ranging from medical image analysis and scene text recognition to multimodal question answering and video narration.