mlsanityData to 14 Oct 2026

DocVQA: A Dataset for VQA on Document Images

arXiv 2007.00398 ↗First posted 1 Jul 2020cs.CV cs.IRcited by 1,317

Not on the map. Why?

Cited by 657 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present a new dataset for Visual Question Answering (VQA) on document images called DocVQA. The dataset consists of 50,000 questions defined on 12,000+ document images. Detailed analysis of the dataset in comparison with similar datasets for VQA and reading comprehension is presented. We report several baseline results by adopting existing VQA and reading comprehension models. Although the existing models perform reasonably well on certain types of questions, there is large performance gap compared to human performance (94.36% accuracy). The models need to improve specifically on questions where understanding structure of the document is crucial. The dataset, code and leaderboard are available at docvqa.org

Key facts. Cited by 1,317 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Jul 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 19; 2022: 31; 2023: 90; 2024: 282; 2025: 411; 2026: 484.

1,317 papers.

Sort:

By area:

  1. 2025Qwen3-VL Technical Reportcited by 2,793
  2. 2024LLaVA-OneVision: Easy Visual Task Transfercited by 2,719
  3. 2023Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyondcited by 2,224
  4. 2025InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Modelscited by 1,834
  5. 2025Gemma 3 Technical Reportcited by 1,824
  6. 2025InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiencycited by 1,510
  7. 2023MVBench: A Comprehensive Multi-modal Video Understanding Benchmarkcited by 1,109
  8. 2024How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suitescited by 1,050
  9. 2024Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMscited by 838
  10. 2023LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attentioncited by 781
  11. 2024Emu3: Next-Token Prediction is All You Needcited by 781
  12. 2025Qwen2.5-Omni Technical Reportcited by 736
38 more
  1. 2023CogAgent: A Visual Language Model for GUI Agentscited by 704
  2. 2025Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Modelscited by 665
  3. 2023LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Modelcited by 602
  4. 2025VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understandingcited by 518
  5. 2024Long Context Transfer from Language to Visioncited by 515
  6. 2022LayoutLMv3: Pre-training for Document AI with Unified Text and Image Maskingcited by 485
  7. 2023OCRBench: On the Hidden Mystery of OCR in Large Multimodal Modelscited by 476
  8. 2024VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Modelscited by 455
  9. 2024LLaVA-CoT: Let Vision Language Models Reason Step-by-Stepcited by 454
  10. 2021InfographicVQAcited by 437
  11. 2021OCR-free Document Understanding Transformercited by 407
  12. 2022Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understandingcited by 366
  13. 2024Mini-Gemini: Mining the Potential of Multi-modality Vision Language Modelscited by 365
  14. 2023Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Modelscited by 357
  15. 2024LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Modelscited by 353
  16. 2024Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Modelscited by 329
  17. 2024What matters when building vision-language models?cited by 326
  18. 2025Perception Encoder: The best visual embeddings are not at the output of the networkcited by 326
  19. 2024SpatialRGPT: Grounded Spatial Reasoning in Vision Language Modelscited by 311
  20. 2024PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reductioncited by 294
  21. 2024MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?cited by 292
  22. 2024We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?cited by 286
  23. 2024Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoningcited by 280
  24. 2024MM-LLMs: Recent Advances in MultiModal Large Language Modelscited by 278
  25. 2023LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understandingcited by 274
  26. 2023PaLI-X: On Scaling up a Multilingual Vision and Language Modelcited by 255
  27. 2025LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Trainingcited by 244
  28. 2024Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimizationcited by 239
  29. 2025SmolVLM: Redefining small and efficient multimodal modelscited by 237
  30. 2024MM1: Methods, Analysis & Insights from Multimodal LLM Pre-trainingcited by 232
  31. 2024VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Taskscited by 228
  32. 2024mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understandingcited by 220
  33. 2023LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Modelscited by 217
  34. 2024CogVLM2: Visual Language Models for Image and Video Understandingcited by 217
  35. 2024MANTIS: Interleaved Multi-Image Instruction Tuningcited by 216
  36. 2025SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Modelscited by 212
  37. 2024NVILA: Efficient Frontier Visual Language Modelscited by 209
  38. 2024PaliGemma 2: A Family of Versatile VLMs for Transfercited by 207

50 most cited of 1,317 All 1,317 (JSON)