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2026-03-05 17:42:51
raw text
AutoFlow AutoFlow: Learning a Better Training Set for Optical Flow CVPR 2021 Disentangling Architecture and Training for Optical Flow ECCV 2022 Self-supervised AutoFlow CVPR 2023 Self-supervised AutoFlow Hsin-Ping Huang Charles Herrmann Junhwa Hur Erika Lu Kyle Sargent Austin Stone Ming-Hsuan Yang Deqing Sun Google Research | Paper | Code | Self-supervised AutoFlow learns to generate an optical flow training set through self-supervision on the target domain. It performs comparable to supervised AutoFlow on Sintel and KITTI without requiring ground truth (GT) and learns a better dataset for real-world DAVIS, where GT is not available. We report optical flow accuracy on Sintel and KITTI, and keypoint propagation accuracy on DAVIS. Abstract Recently, AutoFlow has shown promising results on learning a training set for optical flow, but requires ground truth labels in the target domain to compute its search metric. Observing a strong co...
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