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• W2950241618 abstract "Modern optical flow methods are often composed of a cascade of many independent steps or formulated as a black box neural network that is hard to interpret and analyze. In this work we seek for a plain, interpretable, but learnable solution. We propose a novel inpainting based algorithm that approaches the problem in three steps: feature selection and matching, selection of supporting points and energy based inpainting. To facilitate the inference we propose an optimization layer that allows to backpropagate through 10K iterations of a first-order method without any numerical or memory problems. Compared to recent state-of-the-art networks, our modular CNN is very lightweight and competitive with other, more involved, inpainting based methods." @default.
• W2950241618 created "2019-06-27" @default.
• W2950241618 creator A5032205387 @default.
• W2950241618 creator A5042711268 @default.
• W2950241618 creator A5069159614 @default.
• W2950241618 date "2018-11-09" @default.
• W2950241618 modified "2023-09-27" @default.
• W2950241618 title "Learning Energy Based Inpainting for Optical Flow" @default.
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• W2950241618 hasPublicationYear "2018" @default.
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