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FlowNet: Learning Optical Flow With Convolutional Networks

FlowNet: Learning Optical Flow with Convolutional NetworksAlexey Dosovitskiy , Philipp Fischer , Eddy Ilg , Philip H ausser, Caner Haz rbas , Vladimir Golkov University of FreiburgTechnical University of van der SmagtTechnical University of CremersTechnical University of BroxUniversity of neural Networks (CNNs) have recentlybeen very successful in a variety of computer vision tasks,especially on those linked to recognition. Optical flow esti-mation has not been among the tasks CNNs succeeded at. Inthis paper we construct CNNs which are capable of solvingthe Optical flow estimation problem as a supervised learningtask.

with factored gated restricted Boltzmann machines. Konda and Memisevic [23] use a special autoencoder called ‘syn-chrony autoencoder’. While these approaches work well in a controlled setup and learn features useful for activity recognition in videos, they are not competitive with classi-cal methods on realistic videos. Convolutional Networks.

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