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• W4386076228 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 correlation between the ground truth search metric and self-supervised losses, we introduce self-supervised AutoFlow to handle real-world videos without ground truth labels. Using self-supervised loss as the search metric, our self-supervised AutoFlow performs on par with AutoFlow on Sintel and KITTI where ground truth is available, and performs better on the real-world DAVIS dataset. We further explore using self-supervised AutoFlow in the (semi-)supervised setting and obtain competitive results against the state of the art." @default.
• W4386076228 created "2023-08-23" @default.
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• W4386076228 date "2023-06-01" @default.
• W4386076228 modified "2023-09-27" @default.
• W4386076228 title "Self-supervised AutoFlow" @default.
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• W4386076228 doi "https://doi.org/10.1109/cvpr52729.2023.01098" @default.
• W4386076228 hasPublicationYear "2023" @default.
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