mlsanityData to 7 Oct 2026

InfographicVQA

arXiv 2104.12756 ↗First posted 26 Apr 2021cs.CV cs.CLcited by 430

Not on the map. Why?

Cited by 246 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Infographics are documents designed to effectively communicate information using a combination of textual, graphical and visual elements. In this work, we explore the automatic understanding of infographic images by using Visual Question Answering technique.To this end, we present InfographicVQA, a new dataset that comprises a diverse collection of infographics along with natural language questions and answers annotations. The collected questions require methods to jointly reason over the document layout, textual content, graphical elements, and data visualizations. We curate the dataset with emphasis on questions that require elementary reasoning and basic arithmetic skills. Finally, we evaluate two strong baselines based on state of the art multi-modal VQA models, and establish baseline performance for the new task. The dataset, code and leaderboard will be made available at http://docvqa.org

Key facts. Cited by 430 papers in the map (data through 7 Oct 2026). Builds on 37 of its 57 references that are in the map. First posted on arXiv 26 Apr 2021.

Builds on

37 of 57 references.

References · 37

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  5. 2017Mask R-CNNcited by 7,259
  6. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  7. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  8. 2016Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotationscited by 2,646
  9. 2016Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answeringcited by 2,498
  10. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  11. 2019Towards VQA Models That Can Readcited by 1,696
  12. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
25 more
  1. 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,310
  2. 2019VisualBERT: A Simple and Performant Baseline for Vision and Languagecited by 1,291
  3. 2020DocVQA: A Dataset for VQA on Document Imagescited by 1,288
  4. 2019VL-BERT: Pre-training of Generic Visual-Linguistic Representationscited by 954
  5. 2019DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphscited by 883
  6. 2018VizWiz Grand Challenge: Answering Visual Questions from Blind Peoplecited by 852
  7. 2019LayoutLM: Pre-training of Text and Layout for Document Image Understandingcited by 549
  8. 2020LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understandingcited by 406
  9. 2019PubLayNet: largest dataset ever for document layout analysiscited by 349
  10. 2018DVQA: Understanding Data Visualizations via Question Answeringcited by 338
  11. 2019Scene Text Visual Question Answeringcited by 327
  12. 2017FigureQA: An Annotated Figure Dataset for Visual Reasoningcited by 314
  13. 2015Stacked Attention Networks for Image Question Answeringcited by 273
  14. 2019UNITER: UNiversal Image-TExt Representation Learningcited by 225
  15. 2021VisualMRC: Machine Reading Comprehension on Document Imagescited by 150
  16. 2021Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformercited by 134
  17. 2019Iterative Answer Prediction with Pointer-Augmented Multimodal Transformers for TextVQAcited by 107
  18. 2020TAP: Text-Aware Pre-training for Text-VQA and Text-Captioncited by 95
  19. 2019GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answeringcited by 77
  20. 2018RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipescited by 74
  21. 2019LEAF-QA: Locate, Encode & Attend for Figure Question Answeringcited by 53
  22. 2017Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answeringcited by 31
  23. 2020Are we pretraining it right? Digging deeper into visio-linguistic pretrainingcited by 25
  24. 2018Synthetically Trained Icon Proposals for Parsing and Summarizing Infographicscited by 6
  25. 2017Understanding Infographics through Textual and Visual Tag Predictioncited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 11; 2023: 27; 2024: 105; 2025: 149; 2026: 137.

430 papers.

Sort:

By area:

  1. 2024LLaVA-OneVision: Easy Visual Task Transfercited by 2,669
  2. 2023InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Taskscited by 2,534
  3. 2025Qwen3-VL Technical Reportcited by 2,534
  4. 2025InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Modelscited by 1,769
  5. 2025Gemma 3 Technical Reportcited by 1,686
  6. 2025InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiencycited by 1,419
  7. 2024How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suitescited by 1,033
  8. 2024Emu3: Next-Token Prediction is All You Needcited by 761
  9. 2025Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Modelscited by 653
  10. 2025VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understandingcited by 502
  11. 2023OCRBench: On the Hidden Mystery of OCR in Large Multimodal Modelscited by 466
  12. 2024VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Modelscited by 443
38 more
  1. 2025Kimi-VL Technical Reportcited by 381
  2. 2022Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understandingcited by 360
  3. 2023Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Modelscited by 356
  4. 2024LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Modelscited by 344
  5. 2024What matters when building vision-language models?cited by 321
  6. 2024PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reductioncited by 283
  7. 2024Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoningcited by 267
  8. 2024Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimizationcited by 238
  9. 2024VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documentscited by 237
  10. 2025LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Trainingcited by 233
  11. 2024VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Taskscited by 222
  12. 2024mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understandingcited by 220
  13. 2024CogVLM2: Visual Language Models for Image and Video Understandingcited by 214
  14. 2024NVILA: Efficient Frontier Visual Language Modelscited by 206
  15. 2024InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Outputcited by 195
  16. 2026Gemma 4 Technical Reportcited by 187
  17. 2023SlideVQA: A Dataset for Document Visual Question Answering on Multiple Imagescited by 175
  18. 2024MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizationscited by 174
  19. 2024TextMonkey: An OCR-Free Large Multimodal Model for Understanding Documentcited by 173
  20. 2024VITA: Towards Open-Source Interactive Omni Multimodal LLMcited by 172
  21. 2024Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Searchcited by 171
  22. 2024Building and better understanding vision-language models: insights and future directionscited by 169
  23. 2023mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understandingcited by 167
  24. 2024GME: Improving Universal Multimodal Retrieval by Multimodal LLMscited by 166
  25. 2024OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoningcited by 165
  26. 2023UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoningcited by 158
  27. 2024InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HDcited by 156
  28. 2025LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuningcited by 145
  29. 2023UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Modelcited by 143
  30. 2022Unifying Vision, Text, and Layout for Universal Document Processingcited by 140
  31. 2025Magma: A Foundation Model for Multimodal AI Agentscited by 138
  32. 2024VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language Taskscited by 136
  33. 2024Improve Vision Language Model Chain-of-thought Reasoningcited by 135
  34. 2025VisualPRM: An Effective Process Reward Model for Multimodal Reasoningcited by 134
  35. 2023Document Understanding Dataset and Evaluation (DUDE)cited by 132
  36. 2024SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Modelscited by 130
  37. 2023PaLI-3 Vision Language Models: Smaller, Faster, Strongercited by 125
  38. 2024NVLM: Open Frontier-Class Multimodal LLMscited by 124

50 most cited of 430 All 430 (JSON)