DDFlow: Learning Optical Flow with Unlabeled Data Distillation

Understand

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.