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The FlowNet demonstrated that optical flow estimation can be cast as a learning problem.
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Curriculum learning
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Dense point trajectories by gpu-accelerated large displacement optical flow
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K. Soomro, A. R. Zamir, and M. Shah · 2013
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Fast edge-preserving patchmatch for large displacement optical flow
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Flow fields: Dense correspondence fields for highly accurate large displacement optical flow estimation
C. Bailer, B. Taetz, and D. Stricker · 2015
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Flownet: Learning optical flow with convolutional networks
A. Dosovitskiy, P. Fischer, E. Ilg, P. Häusser, C. Hazırbaş, V. Golkov, P. v.d. Smagt, D. Cremers, and T. Brox · 2015
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Motion trajectory segmentation via minimum cost multicuts
M. Keuper, B. Andres, and T. Brox · 2015
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Epicflow: Edge-preserving interpolation of correspondences for optical flow
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Unsupervised convolutional neural networks for motion estimation
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CNN based patch matching for optical flow with thresholded hinge loss
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Human pose estimation with iterative error feedback
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A naturalistic open source movie for optical flow evaluation
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Learning to extract motion from videos in convolutional neural networks
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