Creativity: Generating Diverse Questions Using Variational Autoencoders

Unnat Jain, Ziyu Zhang, Alexander G. Schwing

CVPR 2017

doi:10.1109/CVPR.2017.575 /cvpr/2017/jain2017cvpr-creativity/

Abstract

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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Cite

Text

Jain et al. "Creativity: Generating Diverse Questions Using Variational Autoencoders." Conference on Computer Vision and Pattern Recognition, 2017. doi:10.1109/CVPR.2017.575

Markdown

[Jain et al. "Creativity: Generating Diverse Questions Using Variational Autoencoders." Conference on Computer Vision and Pattern Recognition, 2017.](https://mlanthology.org/cvpr/2017/jain2017cvpr-creativity/) doi:10.1109/CVPR.2017.575

BibTeX

@inproceedings{jain2017cvpr-creativity,
  title     = {{Creativity: Generating Diverse Questions Using Variational Autoencoders}},
  author    = {Jain, Unnat and Zhang, Ziyu and Schwing, Alexander G.},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2017},
  doi       = {10.1109/CVPR.2017.575},
  url       = {https://mlanthology.org/cvpr/2017/jain2017cvpr-creativity/}
}