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• W2950872240 abstract "We propose a new pipeline for optical flow computation, based on Deep Learning techniques. We suggest using a Siamese CNN to independently, and in parallel, compute the descriptors of both images. The learned descriptors are then compared efficiently using the L2 norm and do not require network processing of patch pairs. The success of the method is based on an innovative loss function that computes higher moments of the loss distributions for each training batch. Combined with an Approximate Nearest Neighbor patch matching method and a flow interpolation technique, state of the art performance is obtained on the most challenging and competitive optical flow benchmarks." @default.
• W2950872240 created "2019-06-27" @default.
• W2950872240 creator A5022300129 @default.
• W2950872240 creator A5078102229 @default.
• W2950872240 date "2015-12-06" @default.
• W2950872240 modified "2023-09-27" @default.
• W2950872240 title "PatchBatch: a Batch Augmented Loss for Optical Flow" @default.
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• W2950872240 hasPublicationYear "2015" @default.
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