  The interpretation of ego motion and scene change is a fundamental task for
mobile robots. Optical flow information can be employed to estimate motion in
the surroundings. Recently, unsupervised optical flow estimation has become a
research hotspot. However, unsupervised approaches are often easy to be
unreliable on partially occluded or texture-less regions. To deal with this
problem, we propose CoT-AMFlow in this paper, an unsupervised optical flow
estimation approach. In terms of the network architecture, we develop an
adaptive modulation network that employs two novel module types, flow
modulation modules (FMMs) and cost volume modulation modules (CMMs), to remove
outliers in challenging regions. As for the training paradigm, we adopt a
co-teaching strategy, where two networks simultaneously teach each other about
challenging regions to further improve accuracy. Experimental results on the
MPI Sintel, KITTI Flow and Middlebury Flow benchmarks demonstrate that our
CoT-AMFlow outperforms all other state-of-the-art unsupervised approaches,
while still running in real time. Our project page is available at
https://sites.google.com/view/cot-amflow.
