The task of video prediction is forecasting the next frames given some previous frames. Despite much recent progress, this task is still challenging mainly due to high nonlinearity in the spatial domain. To address this issue, we propose a novel architecture, Frequency Domain Transformer Network (FDTN), which is an end-to-end learnable model that estimates and uses the transformations of the signal in the frequency domain. Experimental evaluations show that this approach can outperform some widely used video prediction methods like Video Ladder Network (VLN) and Predictive Gated Pyramids (PGP).
@article{arxiv.1903.00271,
title = {Frequency Domain Transformer Networks for Video Prediction},
author = {Hafez Farazi and Sven Behnke},
journal= {arXiv preprint arXiv:1903.00271},
year = {2019}
}
Comments
Accepted for European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, to appear April 2019