# Automatic Generation of Grounded Visual Questions

arXiv: https://arxiv.org/abs/1612.06530 · HTML: https://mlsanity.com/paper/1612.06530/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2016.

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

- [ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions](https://mlsanity.com/paper/2303.06594/) (2023)
- [All You May Need for VQA are Image Captions](https://mlsanity.com/paper/2205.01883/) (2022)
- [LOVA3: Learning to Visual Question Answering, Asking and Assessment](https://mlsanity.com/paper/2405.14974/) (2024)
- [Q: How to Specialize Large Vision-Language Models to Data-Scarce VQA Tasks? A: Self-Train on Unlabeled Images!](https://mlsanity.com/paper/2306.03932/) (2023)
- [Guiding Visual Question Generation](https://mlsanity.com/paper/2110.08226/) (2021)
- [Unified Questioner Transformer for Descriptive Question Generation in Goal-Oriented Visual Dialogue](https://mlsanity.com/paper/2106.15550/) (2021)
- [Weakly Supervised Grounding for VQA in Vision-Language Transformers](https://mlsanity.com/paper/2207.02334/) (2022)
- [ViTextVQA: A Large-Scale Visual Question Answering Dataset and a Novel Multimodal Feature Fusion Method for Vietnamese Text Comprehension in Images](https://mlsanity.com/paper/2404.10652/) (2024)
- [Look, Read and Ask: Learning to Ask Questions by Reading Text in Images](https://mlsanity.com/paper/2211.12950/) (2022)
- [ConVQG: Contrastive Visual Question Generation with Multimodal Guidance](https://mlsanity.com/paper/2402.12846/) (2024)
- [ECIS-VQG: Generation of Entity-centric Information-seeking Questions from Videos](https://mlsanity.com/paper/2410.09776/) (2024)
- [Visual Question Generation in Bengali](https://mlsanity.com/paper/2310.08187/) (2023)
- [Ask Questions with Double Hints: Visual Question Generation with Answer-awareness and Region-reference](https://mlsanity.com/paper/2407.05100/) (2024)
- [Towards Signboard-Oriented Visual Question Answering: ViSignVQA Dataset, Method and Benchmark](https://mlsanity.com/paper/2512.22218/) (2025)
- [Real-Time Cognitive Evaluation of Online Learners through Automatically Generated Questions](https://mlsanity.com/paper/2106.03036/) (2021)
- [Weakly Supervised Visual Question Answer Generation](https://mlsanity.com/paper/2306.06622/) (2023)
- [When Big Models Train Small Ones: Label-Free Model Parity Alignment for Efficient Visual Question Answering using Small VLMs](https://mlsanity.com/paper/2509.16633/) (2025)
- [VideoChain: A Transformer-Based Framework for Multi-hop Video Question Generation](https://mlsanity.com/paper/2511.08348/) (2025)
- [DDVT: Dynamic Dual-level Vision Transformer Fusion Network for Answer Grounding in Visual Question Answering](https://mlsanity.com/paper/2607.23921/) (2026)

## Abstract (arXiv)

In this paper, we propose the first model to be able to generate visually grounded questions with diverse types for a single image. Visual question generation is an emerging topic which aims to ask questions in natural language based on visual input. To the best of our knowledge, it lacks automatic methods to generate meaningful questions with various types for the same visual input. To circumvent the problem, we propose a model that automatically generates visually grounded questions with varying types. Our model takes as input both images and the captions generated by a dense caption model, samples the most probable question types, and generates the questions in sequel. The experimental results on two real world datasets show that our model outperforms the strongest baseline in terms of both correctness and diversity with a wide margin.