Multimodal Large Language Models

The past decade-plus has seen incredible progress in practical computer vision. Thanks to deep learning, computer vision is dramatically more robust and accessible, and has enabled compelling capabilities in thousands of applications, from automotive safety to healthcare. But today’s widely used deep learning techniques suffer from serious limitations. Often, they struggle when confronted with ambiguity (e.g., are those people fighting or dancing?) or with challenging imaging conditions (e.g., is that shadow in the fog a person or a shrub?). And, for many product developers, computer vision remains out of reach due to the cost and complexity of obtaining the necessary training data, or due to lack of necessary technical skills.

Recent advances in large language models (LLMs) and their variants such as vision language models (VLMs, which comprehend both images and text), hold the key to overcoming these challenges. VLMs are an example of multimodal large language models (MLLMs), which integrate multiple data modalities such as language, images, audio, and video to enable complex cross-modal understanding and generation tasks. MLLMs represent a significant evolution in AI by combining the capabilities of LLMs with multimodal processing to handle diverse inputs and outputs.

The purpose of this portal is to facilitate awareness of, and education regarding, the challenges and opportunities in using LLMs, VLMs, and other types of MLLMs in practical applications — especially applications involving  edge AI and machine perception. The content that follows (which is updated regularly) discusses these topics. As a starting point, we encourage you to watch the recording of the symposium “Your Next Computer Vision Model Might be an LLM: Generative AI and the Move From Large Language Models to Vision Language Models“, sponsored by the Edge AI and Vision Alliance. A preview video of the symposium introduction by Jeff Bier, Founder of the Alliance, follows: