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 utilizing the
Winner-Takes-All loss and show that this can provide complementary hypotheses
and uncertainty estimates efficiently with a single forward pass and without
the need for sampling or ensembles. Finally, we demonstrate the quality of the
different uncertainty estimates, which is clearly above previous confidence
measures on optical flow and allows for interactive frame rates.

本文旨在研究一种基于端到端的监督学习方法，用于提高光流估计领域中的准确性和实时性，并首次比较了多种策略和技术来准确估计局部不确定性，同时介绍了一种新的使用 Winner-Takes-All loss 的网络架构，显示它可以有效地提供互补的假设和不确定性估计，并且质量明显高于以往的光流置信度测量。