  Recently, AutoFlow has shown promising results on learning a training set for
optical flow, but requires ground truth labels in the target domain to compute
its search metric. Observing a strong correlation between the ground truth
search metric and self-supervised losses, we introduce self-supervised AutoFlow
to handle real-world videos without ground truth labels. Using self-supervised
loss as the search metric, our self-supervised AutoFlow performs on par with
AutoFlow on Sintel and KITTI where ground truth is available, and performs
better on the real-world DAVIS dataset. We further explore using
self-supervised AutoFlow in the (semi-)supervised setting and obtain
competitive results against the state of the art.
