We propose a strong baseline model for unsupervised feature learning using video data. By learning to predict missing frames or extrapolate future frames from an input video sequence, the model discovers both spatial and temporal correlations which are useful to represent complex deformations and motion patterns. The models we propose are largely borrowed from the language modeling literature, and adapted to the vision domain by quantizing the space of image patches into a large dictionary. We demonstrate the approach on both a filling and a generation task. For the first time, we show that, after training on natural videos, such a model can predict non-trivial motions over short video sequences.
@article{arxiv.1412.6604,
title = {Video (language) modeling: a baseline for generative models of natural videos},
author = {MarcAurelio Ranzato and Arthur Szlam and Joan Bruna and Michael Mathieu and Ronan Collobert and Sumit Chopra},
journal= {arXiv preprint arXiv:1412.6604},
year = {2016}
}