• Turing Post
  • Posts
  • A Comprehensive List of Resources to Understand Multimodal Models

A Comprehensive List of Resources to Understand Multimodal Models

An Overview of Current Surveys, Models, and Tools in Multimodal AI Research

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), 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.

  • September 2023: "Multimodal Foundation Models: From Specialists to General-Purpose Assistants" focuses on the integration of visual and language capabilities in foundation models. It includes insights into visual understanding, visual generation models, and vision-language pre-training (VLP). Additional resources such as slides and a recording from the CVPR 2023 tutorial on "Recent Advances in Vision Foundation Models" are also available, featuring insights from industry experts at Microsoft and Apple.

  • November 2023: "Multimodal Large Language Models: A Survey" gathers essential resources for understanding and applying multimodal models, including advanced algorithms and key datasets. This paper is designed to equip researchers with tools for experimenting and evaluating AI systems that process multiple data types, enhancing capabilities beyond pure text-based models.

  • January 2024: "A Survey of Resource-efficient LLM and Multimodal Foundation Models" compiles resources focused on the application of multimodal models within resource-efficient frameworks. It highlights contributions from the literature on model architectures and optimization techniques essential for developing AI systems with reduced resource demands. The accompanying GitHub repository provides extensive materials on various model types and system designs for enhancing AI efficiency and scalability.

  • February 2024: "Large Multimodal Agents: A Survey" reviews large multimodal agents (LMAs) that extend large language model capabilities to multimodal domains, enabling AI to handle complex multimodal interactions. It organizes existing research, establishes a framework for evaluation methodologies, and outlines potential applications and future research directions for LMAs. This survey is instrumental in standardizing evaluations and shaping the development of future multimodal agents. The related GitHub repository.

  • February 2024: "The (R)Evolution of Multimodal Large Language Models: A Survey" offers an exhaustive review of Multimodal Large Language Models (MLLMs) that combine visual and textual data for various tasks. It discusses architectural choices, alignment strategies, and training techniques. Compiled resources include training datasets, evaluation benchmarks, and performance comparisons, providing a foundational reference for current capabilities and future advancements in MLLMs.

If you’ve found this article valuable, subscribe for free to our newsletter.

Join the conversation

or to participate.