# V-Doc : Visual questions answers with Documents

arXiv: https://arxiv.org/abs/2205.13724 · HTML: https://mlsanity.com/paper/2205.13724/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 19 references that are in the map.
- First posted on arXiv 27 May 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://mlsanity.com/paper/1908.03557/) (2019)
- [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://mlsanity.com/paper/2012.14740/) (2020)
- [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019)
- [FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents](https://mlsanity.com/paper/1905.13538/) (2019)
- [PlotQA: Reasoning over Scientific Plots](https://mlsanity.com/paper/1909.00997/) (2019)
- [FigureQA: An Annotated Figure Dataset for Visual Reasoning](https://mlsanity.com/paper/1710.07300/) (2017)
- [Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding](https://mlsanity.com/paper/1606.01847/) (2016)
- [Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval](https://mlsanity.com/paper/1502.07058/) (2015)
- [Compositional Attention Networks for Machine Reasoning](https://mlsanity.com/paper/1803.03067/) (2018)
- [VisualMRC: Machine Reading Comprehension on Document Images](https://mlsanity.com/paper/2101.11272/) (2021)
- [Dynamic Fusion with Intra- and Inter- Modality Attention Flow for Visual Question Answering](https://mlsanity.com/paper/1812.05252/) (2018)

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

- [MDocAgent: A Multi-Modal Multi-Agent Framework for Document Understanding](https://mlsanity.com/paper/2503.13964/) (2025)
- [VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2412.10704/) (2024)
- [HRVDA: High-Resolution Visual Document Assistant](https://mlsanity.com/paper/2404.06918/) (2024)
- [PDFVQA: A New Dataset for Real-World VQA on PDF Documents](https://mlsanity.com/paper/2304.06447/) (2023)
- [SimpleDoc: Multi-Modal Document Understanding with Dual-Cue Page Retrieval and Iterative Refinement](https://mlsanity.com/paper/2506.14035/) (2025)
- [Deep Learning based Visually Rich Document Content Understanding: A Survey](https://mlsanity.com/paper/2408.01287/) (2024)
- [Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis](https://mlsanity.com/paper/2208.10970/) (2022)
- [MoLoRAG: Bootstrapping Document Understanding via Multi-modal Logic-aware Retrieval](https://mlsanity.com/paper/2509.07666/) (2025)
- [DocDancer: Towards Agentic Document-Grounded Information Seeking](https://mlsanity.com/paper/2601.05163/) (2026)
- [Structured Attention Matters to Multimodal LLMs in Document Understanding](https://mlsanity.com/paper/2506.21600/) (2025)
- [PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering](https://mlsanity.com/paper/2404.12720/) (2024)
- [3MVRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding](https://mlsanity.com/paper/2402.17983/) (2024)
- [PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals](https://mlsanity.com/paper/2211.15940/) (2022)
- [ALDEN: Reinforcement Learning for Active Navigation and Evidence Gathering in Long Documents](https://mlsanity.com/paper/2510.25668/) (2025)
- [DistilDoc: Knowledge Distillation for Visually-Rich Document Applications](https://mlsanity.com/paper/2406.08226/) (2024)
- [Workshop on Document Intelligence Understanding](https://mlsanity.com/paper/2307.16369/) (2023)
- [Joint Extraction Matters: Prompt-Based Visual Question Answering for Multi-Field Document Information Extraction](https://mlsanity.com/paper/2503.16868/) (2025)
- [What Does Your Short-Answer VQA Score Actually Measure? Evaluator-Dependent Instability in Multimodal Short-Answer Benchmarks](https://mlsanity.com/paper/2607.10240/) (2026)
- [D2-ScaleAgent: Dual-Dimensional Scaling for Long Document Understanding](https://mlsanity.com/paper/2608.16417/) (2026)

## Abstract (arXiv)

We propose V-Doc, a question-answering tool using document images and PDF, mainly for researchers and general non-deep learning experts looking to generate, process, and understand the document visual question answering tasks. The V-Doc supports generating and using both extractive and abstractive question-answer pairs using documents images. The extractive QA selects a subset of tokens or phrases from the document contents to predict the answers, while the abstractive QA recognises the language in the content and generates the answer based on the trained model. Both aspects are crucial to understanding the documents, especially in an image format. We include a detailed scenario of question generation for the abstractive QA task. V-Doc supports a wide range of datasets and models, and is highly extensible through a declarative, framework-agnostic platform.