# K-VQG: Knowledge-aware Visual Question Generation for Common-sense Acquisition

arXiv: https://arxiv.org/abs/2203.07890 · HTML: https://mlsanity.com/paper/2203.07890/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 31 references that are in the map.
- First posted on arXiv 15 Mar 2022.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge](https://mlsanity.com/paper/1906.00067/) (2019)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [FVQA: Fact-based Visual Question Answering](https://mlsanity.com/paper/1606.05433/) (2016)
- [Generating Natural Questions About an Image](https://mlsanity.com/paper/1603.06059/) (2016)
- [Visual Question Generation as Dual Task of Visual Question Answering](https://mlsanity.com/paper/1709.07192/) (2017)
- [Information Maximizing Visual Question Generation](https://mlsanity.com/paper/1903.11207/) (2019)
- [CRIC: A VQA Dataset for Compositional Reasoning on Vision and Commonsense](https://mlsanity.com/paper/1908.02962/) (2019)
- [Learning by Asking Questions](https://mlsanity.com/paper/1712.01238/) (2017)
- [Creativity: Generating Diverse Questions using Variational Autoencoders](https://mlsanity.com/paper/1704.03493/) (2017)
- [iVQA: Inverse Visual Question Answering](https://mlsanity.com/paper/1710.03370/) (2017)
- [C3VQG: Category Consistent Cyclic Visual Question Generation](https://mlsanity.com/paper/2005.07771/) (2020)
- [Visual Question Generation for Class Acquisition of Unknown Objects](https://mlsanity.com/paper/1808.01821/) (2018)

## Built on by (most cited first, 9 of 9)

- [ConVQG: Contrastive Visual Question Generation with Multimodal Guidance](https://mlsanity.com/paper/2402.12846/) (2024)
- [Learning by Asking Questions for Knowledge-based Novel Object Recognition](https://mlsanity.com/paper/2210.05879/) (2022)
- [Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation](https://mlsanity.com/paper/2401.10005/) (2024)
- [Diagram-Driven Course Questions Generation](https://mlsanity.com/paper/2411.17771/) (2024)
- [Questions beyond Pixels: Integrating Commonsense Knowledge in Visual Question Generation for Remote Sensing](https://mlsanity.com/paper/2602.19217/) (2026)
- [Toward Ownership Understanding of Objects: Active Question Generation with Large Language Model and Probabilistic Generative Model](https://mlsanity.com/paper/2509.12754/) (2025)
- [Knowledge-aware Visual Question Generation for Remote Sensing Images](https://mlsanity.com/paper/2602.19224/) (2026)
- [Self-Evolving Visual Questioner](https://mlsanity.com/paper/2606.13929/) (2026)
- [Evaluating the Capability of Video Question Generation for Expert Knowledge Elicitation](https://mlsanity.com/paper/2512.15006/) (2025)

## Abstract (arXiv)

Visual Question Generation (VQG) is a task to generate questions from images. When humans ask questions about an image, their goal is often to acquire some new knowledge. However, existing studies on VQG have mainly addressed question generation from answers or question categories, overlooking the objectives of knowledge acquisition. To introduce a knowledge acquisition perspective into VQG, we constructed a novel knowledge-aware VQG dataset called K-VQG. This is the first large, humanly annotated dataset in which questions regarding images are tied to structured knowledge. We also developed a new VQG model that can encode and use knowledge as the target for a question. The experiment results show that our model outperforms existing models on the K-VQG dataset.