# FigureNet: A Deep Learning model for Question-Answering on Scientific Plots

arXiv: https://arxiv.org/abs/1806.04655 · HTML: https://mlsanity.com/paper/1806.04655/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [Chart Question Answering: State of the Art and Future Directions](https://mlsanity.com/paper/2205.03966/) (2022)
- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [mChartQA: A universal benchmark for multimodal Chart Question Answer based on Vision-Language Alignment and Reasoning](https://mlsanity.com/paper/2404.01548/) (2024)
- [DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding](https://mlsanity.com/paper/2310.18983/) (2023)
- [Making Table Understanding Work in Practice](https://mlsanity.com/paper/2109.05173/) (2021)
- [Multimodal Information Fusion for Chart Understanding: A Survey of MLLMs -- Evolution, Limitations, and Cognitive Enhancement](https://mlsanity.com/paper/2602.10138/) (2026)

## Abstract (arXiv)

Deep Learning has managed to push boundaries in a wide variety of tasks. One area of interest is to tackle problems in reasoning and understanding, with an aim to emulate human intelligence. In this work, we describe a deep learning model that addresses the reasoning task of question-answering on categorical plots. We introduce a novel architecture FigureNet, that learns to identify various plot elements, quantify the represented values and determine a relative ordering of these statistical values. We test our model on the FigureQA dataset which provides images and accompanying questions for scientific plots like bar graphs and pie charts, augmented with rich annotations. Our approach outperforms the state-of-the-art Relation Networks baseline by approximately 7% on this dataset, with a training time that is over an order of magnitude lesser.