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DDFlow: Learning Optical Flow with Unlabeled Data Distillation
We present DDFlow, a data distillation approach to learning optical flow
estimation from unlabeled data. The approach distills reliable predictions from
a teacher network, and uses these predictions as annotations to guide a student
network to learn optical flow. Unlike existing work relying on hand-crafted
energy terms to handle occlusion, our approach is data-driven, and learns
optical flow for occluded pixels. This enables us to train our model with a
much simpler loss function, and achieve a much higher accuracy. We conduct a
rigorous evaluation on the challenging Flying Chairs, MPI Sintel, KITTI 2012
and 2015 benchmarks, and show that our approach significantly outperforms all
existing unsupervised learning methods, while running at real time.Comment: 8 pages, AAAI 1
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