CoT-AMFlow: Adaptive Modulation Network with Co-Teaching Strategy for
Unsupervised Optical Flow Estimation
Abstract
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.