Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow
Eddy Ilg, Ozgun Cicek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox
Abstract
Optical flow estimation can be formulated as an end-to-end supervised learning problem, which yields estimates with a superior accuracy-runtime tradeoff compared to alternative methodology. In this paper, we make such networks estimate their local uncertainty about the correctness of their prediction, which is vital information when building decisions on top of the estimations. For the first time we compare several strategies and techniques to estimate uncertainty in a large-scale computer vision task like optical flow estimation. Moreover, we introduce a new network architecture and loss function that enforce complementary hypotheses and provide uncertainty estimates efficiently with a single forward pass and without the need for sampling or ensembles. We demonstrate the quality of the uncertainty estimates, which is clearly above previous confidence measures on optical flow and allows for interactive frame rates.
BibTeX
@inproceedings{eccv2018_uncertaintyestim,
title = {Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow},
author = {Eddy Ilg and Ozgun Cicek and Silvio Galesso and Aaron Klein and Osama Makansi and Frank Hutter and Thomas Brox},
booktitle = {ECCV 2018},
year = {2018}
}