VQA and Image Chunking for MLLMs (GPT-4V and Gemini)

Handling large images with resolutions higher than 2048x2048 remains a challenge for MLLMs.

Image chunking splits large images into multiple smaller images. Then the model processes each image and uses information from all of them to answer a question.

I managed to get it to work with a 3840px X 20294px web page screenshot. It also works with documents that have figures (tables, charts, illustrations, etc).