ScopeFlow: Dynamic Scene Scoping for Optical Flow
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We propose to modify the common training protocols of optical flow, leading to sizable accuracy improvements without adding to the computational complexity of the training process. The improvement is based on observing the bias in sampling challenging data that exists in the current training protocol, and improving the sampling process. In addition, we find that both regularization and augmentation should decrease during the training protocol. Using a low parameters off-the-shelf model, the method is ranked first on the MPI Sintel benchmark among all other methods, improving the best two frames method accuracy by more than 10 architecture variants by more than 12 achieving the lowest Average End-Point Error on KITTI2012 among two-frame methods, without using extra datasets.
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