Creativity: Generating Diverse Questions using Variational Autoencoders

CVPR 2017  ·  Unnat Jain, Ziyu Zhang, Alexander Schwing ·

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.

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