mlsanityData to 7 Oct 2026

Answering Questions about Data Visualizations using Efficient Bimodal Fusion

arXiv 1908.01801 ↗First posted 5 Aug 2019cs.CV cs.AI cs.CLcited by 26

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Abstract (arXiv)

Chart question answering (CQA) is a newly proposed visual question answering (VQA) task where an algorithm must answer questions about data visualizations, e.g. bar charts, pie charts, and line graphs. CQA requires capabilities that natural-image VQA algorithms lack: fine-grained measurements, optical character recognition, and handling out-of-vocabulary words in both questions and answers. Without modifications, state-of-the-art VQA algorithms perform poorly on this task. Here, we propose a novel CQA algorithm called parallel recurrent fusion of image and language (PReFIL). PReFIL first learns bimodal embeddings by fusing question and image features and then intelligently aggregates these learned embeddings to answer the given question. Despite its simplicity, PReFIL greatly surpasses state-of-the art systems and human baselines on both the FigureQA and DVQA datasets. Additionally, we demonstrate that PReFIL can be used to reconstruct tables by asking a series of questions about a chart.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Aug 2019.

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Citing papers per year: 2021: 3; 2022: 4; 2023: 9; 2024: 7; 2025: 2; 2026: 1.

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  1. 2022ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoningcited by 1,449
  2. 2022MapQA: A Dataset for Question Answering on Choropleth Mapscited by 82
  3. 2024From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Modelscited by 52
  4. 2023Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checkingcited by 34
  5. 2024EvoChart: A Benchmark and a Self-Training Approach Towards Real-World Chart Understandingcited by 32
  6. 2022Chart Question Answering: State of the Art and Future Directionscited by 26
  7. 2023ChartCheck: Explainable Fact-Checking over Real-World Chart Imagescited by 25
  8. 2023ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rulescited by 24
  9. 2023Visual Question Answering: A Survey on Techniques and Common Trends in Recent Literaturecited by 20
  10. 2022LineCap: Line Charts for Data Visualization Captioning Modelscited by 17
  11. 2023Enhanced Chart Understanding in Vision and Language Task via Cross-modal Pre-training on Plot Table Pairscited by 15
  12. 2023DOMINO: A Dual-System for Multi-step Visual Language Reasoningcited by 12
14 more
  1. 2021Classification-Regression for Chart Comprehensioncited by 11
  2. 2025Time Series Language Model for Descriptive Caption Generationcited by 11
  3. 2024mChartQA: A universal benchmark for multimodal Chart Question Answer based on Vision-Language Alignment and Reasoningcited by 9
  4. 2023FigCaps-HF: A Figure-to-Caption Generative Framework and Benchmark with Human Feedbackcited by 7
  5. 2024VProChart: Answering Chart Question through Visual Perception Alignment Agent and Programmatic Solution Reasoningcited by 6
  6. 2024Revisiting Multi-Modal LLM Evaluationcited by 5
  7. 2023DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understandingcited by 3
  8. 2023BloomVQA: Assessing Hierarchical Multi-modal Comprehensioncited by 3
  9. 2025ChartLens: Fine-grained Visual Attribution in Chartscited by 3
  10. 2021CHARTER: heatmap-based multi-type chart data extractioncited by 2
  11. 2024GoT-CQA: Graph-of-Thought Guided Compositional Reasoning for Chart Question Answeringcited by 2
  12. 2024ChartKG: A Knowledge-Graph-Based Representation for Chart Imagescited by 2
  13. 2026Multimodal Information Fusion for Chart Understanding: A Survey of MLLMs -- Evolution, Limitations, and Cognitive Enhancementcited by 1
  14. 2021Q4EDA: A Novel Strategy for Textual Information Retrieval Based on User Interactions with Visual Representations of Time Seriescited by 0