  Existing recurrent optical flow estimation networks are computationally
expensive since they use a fixed large number of iterations to update the flow
field for each sample. An efficient network should skip iterations when the
flow improvement is limited. In this paper, we develop a Context-Aware
Iteration Policy Network for efficient optical flow estimation, which
determines the optimal number of iterations per sample. The policy network
achieves this by learning contextual information to realize whether flow
improvement is bottlenecked or minimal. On the one hand, we use iteration
embedding and historical hidden cell, which include previous iterations
information, to convey how flow has changed from previous iterations. On the
other hand, we use the incremental loss to make the policy network implicitly
perceive the magnitude of optical flow improvement in the subsequent iteration.
Furthermore, the computational complexity in our dynamic network is
controllable, allowing us to satisfy various resource preferences with a single
trained model. Our policy network can be easily integrated into
state-of-the-art optical flow networks. Extensive experiments show that our
method maintains performance while reducing FLOPs by about 40%/20% for the
Sintel/KITTI datasets.
