Multimodal Foundation Model

Multimodal foundation models integrate multiple data modalities, such as text, images, and audio, to create powerful AI systems capable of complex tasks. Current research emphasizes improving model performance through optimized data preprocessing (e.g., caption generation for image-text alignment), efficient utilization of existing models for new data types (e.g., representing time series data as plots), and mitigating limitations like hallucination and bias. These advancements are significant because they enable more robust and versatile AI applications across diverse fields, including healthcare, autonomous driving, and scientific discovery, while also addressing critical issues of resource efficiency and ethical concerns.