ML Anthology

Occlusion Aware Unsupervised Learning of Optical Flow

Yang Wang, Yi Yang, Zhenheng Yang, Liang Zhao, Peng Wang, Wei Xu
CVPR 2018
doi:10.1109/CVPR.2018.00513 /cvpr/2018/wang2018cvpr-occlusion/

Abstract

It has been recently shown that a convolutional neural network can learn optical flow estimation with unsuper- vised learning. However, the performance of the unsuper- vised methods still has a relatively large gap compared to its supervised counterpart. Occlusion and large motion are some of the major factors that limit the current unsuper- vised learning of optical flow methods. In this work we introduce a new method which models occlusion explicitly and a new warping way that facilitates the learning of large motion. Our method shows promising results on Flying Chairs, MPI-Sintel and KITTI benchmark datasets. Espe- cially on KITTI dataset where abundant unlabeled samples exist, our unsupervised method outperforms its counterpart trained with supervised learning.

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Cite

Text

Wang et al. "Occlusion Aware Unsupervised Learning of Optical Flow." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00513

Markdown

[Wang et al. "Occlusion Aware Unsupervised Learning of Optical Flow." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/wang2018cvpr-occlusion/) doi:10.1109/CVPR.2018.00513

BibTeX

@inproceedings{wang2018cvpr-occlusion,
  title     = {{Occlusion Aware Unsupervised Learning of Optical Flow}},
  author    = {Wang, Yang and Yang, Yi and Yang, Zhenheng and Zhao, Liang and Wang, Peng and Xu, Wei},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2018},
  doi       = {10.1109/CVPR.2018.00513},
  url       = {https://mlanthology.org/cvpr/2018/wang2018cvpr-occlusion/}
}