Visual language data such as plots, charts, and infographics are ubiquitous in the human world. However, state-of-the-art vision-language models do not perform well on these data.
We proposeMatCha(Math reasoning andChart derendering pretraining)  to enhance visual language models’ capabilities in jointly modeling charts/plots and language data. Specifically we propose several pretraining tasks that cover plot deconstruction and numerical reasoning which are the key capabilities in visual language modeling.
We perform theMatChapretraining starting from Pix2Struct, a recently proposed image-to-text visual language model.
On standard benchmarks such as PlotQA and ChartQA, theMatChamodel outperforms state-of-the-art methods by as much as nearly 20%.
We also examine how well theMatChapretraining transfers to domains such as screenshots, textbook diagrams, and document figures and observe overall improvement, verifying the usefulness ofMatChapretraining on broader visual language tasks.111Code and models:github.com/google-research/google-research/tree/master/deplot222For questions paper please contactfl399@cam.ac.ukandeisenjulian@google.com.