Building and better understanding vision-language models: insights and future directions
It offers practical guidance for researchers and practitioners in AI to develop VLMs, but is incremental as it builds on existing models and datasets.
This paper provides a tutorial on building vision-language models (VLMs), addressing key challenges in data, architecture, and training, and demonstrates the development of Idefics3-8B, which significantly outperforms its predecessor Idefics2-8B while using open datasets and an efficient pipeline.
The field of vision-language models (VLMs), which take images and texts as inputs and output texts, is rapidly evolving and has yet to reach consensus on several key aspects of the development pipeline, including data, architecture, and training methods. This paper can be seen as a tutorial for building a VLM. We begin by providing a comprehensive overview of the current state-of-the-art approaches, highlighting the strengths and weaknesses of each, addressing the major challenges in the field, and suggesting promising research directions for underexplored areas. We then walk through the practical steps to build Idefics3-8B, a powerful VLM that significantly outperforms its predecessor Idefics2-8B, while being trained efficiently, exclusively on open datasets, and using a straightforward pipeline. These steps include the creation of Docmatix, a dataset for improving document understanding capabilities, which is 240 times larger than previously available datasets. We release the model along with the datasets created for its training.