  A training pipeline for optical flow CNNs consists of a pretraining stage on
a synthetic dataset followed by a fine tuning stage on a target dataset.
However, obtaining ground truth flows from a target video requires a tremendous
effort. This paper proposes a practical fine tuning method to adapt a
pretrained model to a target dataset without ground truth flows, which has not
been explored extensively. Specifically, we propose a flow supervisor for
self-supervision, which consists of parameter separation and a student output
connection. This design is aimed at stable convergence and better accuracy over
conventional self-supervision methods which are unstable on the fine tuning
task. Experimental results show the effectiveness of our method compared to
different self-supervision methods for semi-supervised learning. In addition,
we achieve meaningful improvements over state-of-the-art optical flow models on
Sintel and KITTI benchmarks by exploiting additional unlabeled datasets. Code
is available at https://github.com/iwbn/flow-supervisor.
