Learn More
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(More)
We show that the matching problem that underlies optical flow requires multiple strategies, depending on the amount of image motion and other factors. We then study the implications of this observation on training a deep neural network for representing image patches in the context of descriptor based optical flow. We propose a metric learning method, which(More)
Visual Learning of Arithmetic Operations Yedid Hoshen and Shmuel Peleg ­ HUJI A simple Neural Network model is presented for end­to­end visual learning of arithmetic operations from pictures of numbers. The input consists of two pictures, each showing a 7­digit number. The output, also a picture, displays the number showing the result of an arithmetic (More)
  • 1