Corpus ID: 233443829

AutoFlow: Learning a Better Training Set for Optical Flow

@inproceedings{Sun2021AutoFlowLA,
  title={AutoFlow: Learning a Better Training Set for Optical Flow},
  author={Deqing Sun and Daniel Vlasic and Charles Herrmann and V. Jampani and Michael Krainin and Huiwen Chang and Ramin Zabih and William T. Freeman and Ce Liu},
  booktitle={CVPR},
  year={2021}
}
Synthetic datasets play a critical role in pre-training CNN models for optical flow, but they are painstaking to generate and hard to adapt to new applications. To automate the process, we present AutoFlow, a simple and effective method to render training data for optical flow that optimizes the performance of a model on a target dataset. AutoFlow takes a layered approach to render synthetic data, where the motion, shape, and appearance of each layer are controlled by learnable hyperparameters… Expand
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