Generating diverse questions for given images is an important task for computational education, entertainment and AI assistants. Different from many conventional prediction techniques is the need for algorithms to generate a diverse set of plausible questions, which we refer to as "creativity". In this paper we propose a creative algorithm for visual question generation which combines the advantages of variational autoencoders with long short-term memory networks. We demonstrate that our framework is able to generate a large set of varying questions given a single input image.
@article{arxiv.1704.03493,
title = {Creativity: Generating Diverse Questions using Variational Autoencoders},
author = {Unnat Jain and Ziyu Zhang and Alexander Schwing},
journal= {arXiv preprint arXiv:1704.03493},
year = {2017}
}