Multimodal Foundation Models: 2024's Surveys to Understand the Future of AI

Multimodal is suggested to be the trend for artificial intelligence research in 2024.

This shift is driven by the inherently multimodal nature of real-world data, which is particularly evident in complex domains like healthcare. In such fields, data types are diverse, ranging from medical images (e.g., X-rays), and structured data (e.g., test results), to clinical text (e.g., patient histories). Multimodal foundation models aim to fuse these diverse information streams, providing a holistic understanding of each domain. This integration facilitates more accurate predictions, better-informed decisions, and deeper insights generated by the models.

For those interested in diving deeper into this technology, read our detailed article:

Additionally, we compiled a list of comprehensive surveys on Multimodal Large Language Models (MLLMs). Each survey covers different aspects of MLLMs and includes valuable resources, such as GitHub repositories with essential links.

We recommend exploring these surveys:

• April 2024: “A Survey on Multimodal Large Language Models” collects a variety of resources including architectural details, training strategies, and datasets related to Multimodal Large Language Models (MLLMs). It provides a detailed understanding of how these models integrate and process multimodal (visual and textual) information, crucial for enhancing model performance in diverse applications. Its associated GitHub repository, which includes links to all resources mentioned in the paper.