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.
@article{arxiv.1612.06530,
title = {Automatic Generation of Grounded Visual Questions},
author = {Shijie Zhang and Lizhen Qu and Shaodi You and Zhenglu Yang and Jiawan Zhang},
journal= {arXiv preprint arXiv:1612.06530},
year = {2017}
}