mlsanityData to 7 Oct 2026

MapQA: A Dataset for Question Answering on Choropleth Maps

arXiv 2211.08545 ↗First posted 15 Nov 2022cs.CV cs.CLcited by 82

Not on the map. Why?

Cited by 54 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Choropleth maps are a common visual representation for region-specific tabular data and are used in a number of different venues (newspapers, articles, etc). These maps are human-readable but are often challenging to deal with when trying to extract data for screen readers, analyses, or other related tasks. Recent research into Visual-Question Answering (VQA) has studied question answering on human-generated charts (ChartQA), such as bar, line, and pie charts. However, little work has paid attention to understanding maps; general VQA models, and ChartQA models, suffer when asked to perform this task. To facilitate and encourage research in this area, we present MapQA, a large-scale dataset of ~800K question-answer pairs over ~60K map images. Our task tests various levels of map understanding, from surface questions about map styles to complex questions that require reasoning on the underlying data. We present the unique challenges of MapQA that frustrate most strong baseline algorithms designed for ChartQA and general VQA tasks. We also present a novel algorithm, Visual Multi-Output Data Extraction based QA (V-MODEQA) for MapQA. V-MODEQA extracts the underlying structured data from a map image with a multi-output model and then performs reasoning on the extracted data. Our experimental results show that V-MODEQA has better overall performance and robustness on MapQA than the state-of-the-art ChartQA and VQA algorithms by capturing the unique properties in map question answering.

Key facts. Cited by 82 papers in the map (data through 7 Oct 2026). Builds on 30 of its 47 references that are in the map. First posted on arXiv 15 Nov 2022.

Builds on

30 of 47 references.

References · 30

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2016Densely Connected Convolutional Networkscited by 6,691
  5. 2016Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotationscited by 2,646
  6. 2016Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answeringcited by 2,498
  7. 2015VQA: Visual Question Answeringcited by 2,489
  8. 2016CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoningcited by 1,445
  9. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,421
  10. 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,310
  11. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  12. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
18 more
  1. 2016A Diagram Is Worth A Dozen Imagescited by 793
  2. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  3. 2015Cyclical Learning Rates for Training Neural Networkscited by 627
  4. 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 432
  5. 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 425
  6. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  7. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
  8. 2018DVQA: Understanding Data Visualizations via Question Answeringcited by 338
  9. 2019PlotQA: Reasoning over Scientific Plotscited by 317
  10. 2017FigureQA: An Annotated Figure Dataset for Visual Reasoningcited by 314
  11. 2015Stacked Attention Networks for Image Question Answeringcited by 273
  12. 2021IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoningcited by 262
  13. 2016Visual Question Answering: A Survey of Methods and Datasetscited by 123
  14. 2014A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Inputcited by 112
  15. 2019LEAF-QA: Locate, Encode & Attend for Figure Question Answeringcited by 53
  16. 2017Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answeringcited by 31
  17. 2019Answering Questions about Data Visualizations using Efficient Bimodal Fusioncited by 26
  18. 2016Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Servicescited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 21; 2025: 35; 2026: 23.

82 papers.

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  1. 2024LLaVA-OneVision: Easy Visual Task Transfercited by 2,669
  2. 2025InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Modelscited by 1,769
  3. 2023MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contextscited by 1,582
  4. 2024Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scalingcited by 1,573
  5. 2024What matters when building vision-language models?cited by 321
  6. 2025LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RLcited by 244
  7. 2024Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimizationcited by 238
  8. 2024Building and better understanding vision-language models: insights and future directionscited by 169
  9. 2025VisualPRM: An Effective Process Reward Model for Multimodal Reasoningcited by 134
  10. 2024Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Modelscited by 133
  11. 2023Document Understanding Dataset and Evaluation (DUDE)cited by 132
  12. 2024MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scalecited by 125
38 more
  1. 2025OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cyclescited by 122
  2. 2025Baichuan-Omni-1.5 Technical Reportcited by 107
  3. 2024Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performancecited by 94
  4. 2025Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Modelscited by 69
  5. 2024From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Modelscited by 52
  6. 2025FineVision: Open Data Is All You Needcited by 51
  7. 2024MMInstruct: A High-Quality Multi-Modal Instruction Tuning Dataset with Extensive Diversitycited by 38
  8. 2025VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Searchcited by 34
  9. 2025SlowFast-LLaVA-1.5: A Family of Token-Efficient Video Large Language Models for Long-Form Video Understandingcited by 30
  10. 2025InteractiveOmni: A Unified Omni-modal Model for Audio-Visual Multi-turn Dialoguecited by 27
  11. 2024BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devicescited by 25
  12. 2024PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Modelscited by 25
  13. 2024Polymath: A Challenging Multi-modal Mathematical Reasoning Benchmarkcited by 22
  14. 2025MLLM-CL: Continual Learning for Multimodal Large Language Modelscited by 21
  15. 2026LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decodingcited by 20
  16. 2025M2-omni: Advancing Omni-MLLM for Comprehensive Modality Support with Competitive Performancecited by 19
  17. 2024Leopard: A Vision Language Model For Text-Rich Multi-Image Taskscited by 18
  18. 2024POINTS: Improving Your Vision-language Model with Affordable Strategiescited by 17
  19. 2025NVIDIA Nemotron Nano V2 VLcited by 15
  20. 2024MMRo: Are Multimodal LLMs Eligible as the Brain for In-Home Robotics?cited by 14
  21. 2025Mixed-R1: Unified Reward Perspective For Reasoning Capability in Multimodal Large Language Modelscited by 13
  22. 2025Vision-G1: Towards General Vision Language Reasoning with Multi-Domain Data Curationcited by 13
  23. 2024Chimera: Improving Generalist Model with Domain-Specific Expertscited by 11
  24. 2025Skip-Vision: Efficient and Scalable Acceleration of Vision-Language Models via Adaptive Token Skippingcited by 11
  25. 2025Corvid: Improving Multimodal Large Language Models Towards Chain-of-Thought Reasoningcited by 11
  26. 2024OmChat: A Recipe to Train Multimodal Language Models with Strong Long Context and Video Understandingcited by 10
  27. 2024HoVLE: Unleashing the Power of Monolithic Vision-Language Models with Holistic Vision-Language Embeddingcited by 10
  28. 2025MCITlib: Multimodal Continual Instruction Tuning Library and Benchmarkcited by 10
  29. 2025On the Perception Bottleneck of VLMs for Chart Understandingcited by 9
  30. 2025MLLM-Selector: Necessity and Diversity-driven High-Value Data Selection for Enhanced Visual Instruction Tuningcited by 9
  31. 2024CompCap: Improving Multimodal Large Language Models with Composite Captionscited by 8
  32. 2025Unified Reinforcement and Imitation Learning for Vision-Language Modelscited by 8
  33. 2025LocateAnything3D: Vision-Language 3D Detection with Chain-of-Sightcited by 8
  34. 2023RealCQA: Scientific Chart Question Answering as a Test-bed for First-Order Logiccited by 7
  35. 2025ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answeringcited by 6
  36. 2025Masking Teacher and Reinforcing Student for Distilling Vision-Language Modelscited by 6
  37. 2025MapIQ: Evaluating Multimodal Large Language Models for Map Question Answeringcited by 5
  38. 2025Breaking Language Barriers in Visual Language Models via Multilingual Textual Regularizationcited by 4

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