mlsanityData to 7 Oct 2026

FigureQA: An Annotated Figure Dataset for Visual Reasoning

arXiv 1710.07300 ↗First posted 19 Oct 2017cs.CVcited by 314

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Cited by 144 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce FigureQA, a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images. The images are synthetic, scientific-style figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts. We formulate our reasoning task by generating questions from 15 templates; questions concern various relationships between plot elements and examine characteristics like the maximum, the minimum, area-under-the-curve, smoothness, and intersection. To resolve, such questions often require reference to multiple plot elements and synthesis of information distributed spatially throughout a figure. To facilitate the training of machine learning systems, the corpus also includes side data that can be used to formulate auxiliary objectives. In particular, we provide the numerical data used to generate each figure as well as bounding-box annotations for all plot elements. We study the proposed visual reasoning task by training several models, including the recently proposed Relation Network as a strong baseline. Preliminary results indicate that the task poses a significant machine learning challenge. We envision FigureQA as a first step towards developing models that can intuitively recognize patterns from visual representations of data.

Key facts. Cited by 314 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Oct 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 14; 2023: 25; 2024: 61; 2025: 109; 2026: 88.

314 papers.

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  1. 2024LLaVA-OneVision: Easy Visual Task Transfercited by 2,669
  2. 2023MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contextscited by 1,582
  3. 2024Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scalingcited by 1,573
  4. 2022ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoningcited by 1,449
  5. 2024MiniCPM-V: A GPT-4V Level MLLM on Your Phonecited by 1,061
  6. 2025Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Modelscited by 653
  7. 2021InfographicVQAcited by 430
  8. 2025R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalizationcited by 364
  9. 2024What matters when building vision-language models?cited by 321
  10. 2024Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Modelscited by 321
  11. 2021IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoningcited by 262
  12. 2025LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RLcited by 244
38 more
  1. 2024CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMscited by 229
  2. 2024mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understandingcited by 220
  3. 2024CogVLM2: Visual Language Models for Image and Video Understandingcited by 214
  4. 2024Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Searchcited by 171
  5. 2024Building and better understanding vision-language models: insights and future directionscited by 169
  6. 2023UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoningcited by 158
  7. 2021VisualMRC: Machine Reading Comprehension on Document Imagescited by 150
  8. 2023MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuningcited by 149
  9. 2025SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvementcited by 144
  10. 2023UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Modelcited by 143
  11. 2024Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Modelscited by 138
  12. 2021Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformercited by 134
  13. 2024Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Modelscited by 133
  14. 2022MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derenderingcited by 132
  15. 2026Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Groundingcited by 132
  16. 2024MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scalecited by 125
  17. 2022A Survey of Deep Learning for Mathematical Reasoningcited by 124
  18. 2024NVLM: Open Frontier-Class Multimodal LLMscited by 124
  19. 2025OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cyclescited by 122
  20. 2024mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understandingcited by 113
  21. 2025Baichuan-Omni-1.5 Technical Reportcited by 107
  22. 2023DocPedia: Unleashing the Power of Large Multimodal Model in the Frequency Domain for Versatile Document Understandingcited by 106
  23. 2025ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answeringcited by 105
  24. 2025Towards Scientific Intelligence: A Survey of LLM-based Scientific Agentscited by 96
  25. 2024Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performancecited by 94
  26. 2022MapQA: A Dataset for Question Answering on Choropleth Mapscited by 82
  27. 2021SciCap: Generating Captions for Scientific Figurescited by 76
  28. 2024SPIQA: A Dataset for Multimodal Question Answering on Scientific Paperscited by 75
  29. 2023AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZcited by 74
  30. 2024MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMscited by 72
  31. 2025Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Modelscited by 69
  32. 2025Virgo: A Preliminary Exploration on Reproducing o1-like MLLMcited by 68
  33. 2023SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphscited by 66
  34. 2022ScreenQA: Large-Scale Question-Answer Pairs over Mobile App Screenshotscited by 65
  35. 2022OpenCQA: Open-ended Question Answering with Chartscited by 60
  36. 2025Scaling Text-Rich Image Understanding via Code-Guided Synthetic Multimodal Data Generationcited by 54
  37. 2024From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Modelscited by 52
  38. 2025SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversioncited by 51

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