# ICDAR 2021 Competition on Document VisualQuestion Answering

arXiv: https://arxiv.org/abs/2111.05547 · HTML: https://mlsanity.com/paper/2111.05547/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 41 references that are in the map.
- First posted on arXiv 10 Nov 2021.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://mlsanity.com/paper/1903.00161/) (2019)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [Compositional Semantic Parsing on Semi-Structured Tables](https://mlsanity.com/paper/1508.00305/) (2015)
- [InfographicVQA](https://mlsanity.com/paper/2104.12756/) (2021)
- [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://mlsanity.com/paper/2012.14740/) (2020)
- [DVQA: Understanding Data Visualizations via Question Answering](https://mlsanity.com/paper/1801.08163/) (2018)
- [Scene Text Visual Question Answering](https://mlsanity.com/paper/1905.13648/) (2019)
- [FigureQA: An Annotated Figure Dataset for Visual Reasoning](https://mlsanity.com/paper/1710.07300/) (2017)
- [COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images](https://mlsanity.com/paper/1601.07140/) (2016)
- [Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer](https://mlsanity.com/paper/2102.09550/) (2021)
- [StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding](https://mlsanity.com/paper/1908.04577/) (2019)
- [Iterative Answer Prediction with Pointer-Augmented Multimodal Transformers for TextVQA](https://mlsanity.com/paper/1911.06258/) (2019)
- [GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering](https://mlsanity.com/paper/1902.09506/) (2019)
- [Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge](https://mlsanity.com/paper/1708.02711/) (2017)
- [RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes](https://mlsanity.com/paper/1809.00812/) (2018)
- [LEAF-QA: Locate, Encode & Attend for Figure Question Answering](https://mlsanity.com/paper/1907.12861/) (2019)
- [ICDAR 2019 Competition on Scene Text Visual Question Answering](https://mlsanity.com/paper/1907.00490/) (2019)
- [Document Collection Visual Question Answering](https://mlsanity.com/paper/2104.14336/) (2021)
- [Document Visual Question Answering Challenge 2020](https://mlsanity.com/paper/2008.08899/) (2020)
- [Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps](https://mlsanity.com/paper/2012.05153/) (2020)

## Built on by (most cited first, 19 of 19)

- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [Document Understanding Dataset and Evaluation (DUDE)](https://mlsanity.com/paper/2305.08455/) (2023)
- [Hierarchical multimodal transformers for Multi-Page DocVQA](https://mlsanity.com/paper/2212.05935/) (2022)
- [OCR-IDL: OCR Annotations for Industry Document Library Dataset](https://mlsanity.com/paper/2202.12985/) (2022)
- [Privacy-Aware Document Visual Question Answering](https://mlsanity.com/paper/2312.10108/) (2023)
- [VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification](https://mlsanity.com/paper/2205.12029/) (2022)
- [RealCQA: Scientific Chart Question Answering as a Test-bed for First-Order Logic](https://mlsanity.com/paper/2308.01979/) (2023)
- [On Web-based Visual Corpus Construction for Visual Document Understanding](https://mlsanity.com/paper/2211.03256/) (2022)
- [Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models](https://mlsanity.com/paper/2305.15080/) (2023)
- [TreeForm: End-to-end Annotation and Evaluation for Form Document Parsing](https://mlsanity.com/paper/2402.05282/) (2024)
- [Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering](https://mlsanity.com/paper/2607.07179/) (2026)
- [Instruction Makes a Difference](https://mlsanity.com/paper/2402.00453/) (2024)
- [NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA](https://mlsanity.com/paper/2411.03730/) (2024)
- [Federated Document Visual Question Answering: A Pilot Study](https://mlsanity.com/paper/2405.06636/) (2024)
- [DistilDoc: Knowledge Distillation for Visually-Rich Document Applications](https://mlsanity.com/paper/2406.08226/) (2024)
- [KIEval: Evaluation Metric for Document Key Information Extraction](https://mlsanity.com/paper/2503.05488/) (2025)
- [One-Shot Doc Snippet Detection: Powering Search in Document Beyond Text](https://mlsanity.com/paper/2209.06584/) (2022)
- [Preserving Privacy Without Compromising Accuracy: Machine Unlearning for Handwritten Text Recognition](https://mlsanity.com/paper/2504.08616/) (2025)
- [TestHallVQA: Exploring LVLMs' Document-Level Reasoning under Redundant Contexts from Scientific Exams](https://mlsanity.com/paper/2609.13158/) (2026)

## Abstract (arXiv)

In this report we present results of the ICDAR 2021 edition of the Document Visual Question Challenges. This edition complements the previous tasks on Single Document VQA and Document Collection VQA with a newly introduced on Infographics VQA. Infographics VQA is based on a new dataset of more than 5,000 infographics images and 30,000 question-answer pairs. The winner methods have scored 0.6120 ANLS in Infographics VQA task, 0.7743 ANLSL in Document Collection VQA task and 0.8705 ANLS in Single Document VQA. We present a summary of the datasets used for each task, description of each of the submitted methods and the results and analysis of their performance. A summary of the progress made on Single Document VQA since the first edition of the DocVQA 2020 challenge is also presented.