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- W4313016775 abstract "Imposing consistency through proxy tasks has been shown to enhance data-driven learning and enable self-supervision in various tasks. This paper introduces novel and effective consistency strategies for optical flow estimation, a problem where labels from real-world data are very challenging to derive. More specifically, we propose occlusion consistency and zero forcing in the forms of self-supervised learning and transformation consistency in the form of semi-supervised learning. We apply these consistency techniques in a way that the network model learns to describe pixel-level motions better while requiring no additional annotations. We demonstrate that our consistency strategies applied to a strong baseline network model using the original datasets and labels provide further improvements, attaining the state-of-the-art results on the KITTI-2015 scene flow benchmark in the non-stereo category. Our method achieves the best foreground accuracy (4.33% in Fl-all) over both the stereo and non-stereo categories, even though using only monocular image inputs." @default.
- W4313016775 created "2023-01-05" @default.
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- W4313016775 date "2022-06-01" @default.
- W4313016775 modified "2023-09-30" @default.
- W4313016775 title "Imposing Consistency for Optical Flow Estimation" @default.
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- W4313016775 doi "https://doi.org/10.1109/cvpr52688.2022.00318" @default.
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