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M.: Patch to the future: Unsupervised visual prediction
| Venue: | In: CVPR. (2014 |
| Citations: | 8 - 0 self |
BibTeX
@INPROCEEDINGS{Walker_m.:patch,
author = {Jacob Walker and Abhinav Gupta and Martial Hebert},
title = {M.: Patch to the future: Unsupervised visual prediction},
booktitle = {In: CVPR. (2014},
year = {}
}
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Abstract
In this paper we present a conceptually simple but sur-prisingly powerful method for visual prediction which com-bines the effectiveness of mid-level visual elements with temporal modeling. Our framework can be learned in a completely unsupervised manner from a large collection of videos. However, more importantly, because our approach models the prediction framework on these mid-level ele-ments, we can not only predict the possible motion in the scene but also predict visual appearances — how are ap-pearances going to change with time. This yields a visual “hallucination ” of probable events on top of the scene. We show that our method is able to accurately predict and visu-alize simple future events; we also show that our approach is comparable to supervised methods for event prediction. 1.
