  Supervised training of optical flow predictors generally yields better
accuracy than unsupervised training. However, the improved performance comes at
an often high annotation cost. Semi-supervised training trades off accuracy
against annotation cost. We use a simple yet effective semi-supervised training
method to show that even a small fraction of labels can improve flow accuracy
by a significant margin over unsupervised training. In addition, we propose
active learning methods based on simple heuristics to further reduce the number
of labels required to achieve the same target accuracy. Our experiments on both
synthetic and real optical flow datasets show that our semi-supervised networks
generally need around 50% of the labels to achieve close to full-label
accuracy, and only around 20% with active learning on Sintel. We also analyze
and show insights on the factors that may influence active learning
performance. Code is available at
https://github.com/duke-vision/optical-flow-active-learning-release.
