Generating Image Sequence from Description with LSTM Conditional GAN
Computer Vision and Pattern Recognition
2018-06-11 v1
Abstract
Generating images from word descriptions is a challenging task. Generative adversarial networks(GANs) are shown to be able to generate realistic images of real-life objects. In this paper, we propose a new neural network architecture of LSTM Conditional Generative Adversarial Networks to generate images of real-life objects. Our proposed model is trained on the Oxford-102 Flowers and Caltech-UCSD Birds-200-2011 datasets. We demonstrate that our proposed model produces the better results surpassing other state-of-art approaches.
Cite
@article{arxiv.1806.03027,
title = {Generating Image Sequence from Description with LSTM Conditional GAN},
author = {Xu Ouyang and Xi Zhang and Di Ma and Gady Agam},
journal= {arXiv preprint arXiv:1806.03027},
year = {2018}
}
Comments
Accepted by ICPR 2018