@inproceedings{fcb4f9f5f3494ba2a28e4417080fde1e,
title = "Optical flow requires multiple strategies (but only one network)",
abstract = "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 selects suitable negative samples based on the nature of the true match. This type of training produces a network that displays multiple strategies depending on the input and leads to state of the art results on the KITTI 2012 and KITTI 2015 optical flow benchmarks.",
author = "Tal Schuster and Lior Wolf and David Gadot",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 ; Conference date: 21-07-2017 Through 26-07-2017",
year = "2017",
month = nov,
day = "6",
doi = "10.1109/CVPR.2017.732",
language = "الإنجليزيّة",
series = "Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6921--6930",
booktitle = "Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017",
address = "الولايات المتّحدة",
}