Deep Extreme Cut: From Extreme Points to Object Segmentation
Abstract
This paper explores the use of extreme points in an object (left-most, right-most, top, bottom pixels) as input to obtain precise object segmentation for images and videos. We do so by adding an extra channel to the image in the input of a convolutional neural network (CNN), which contains a Gaussian centered in each of the extreme points. The CNN learns to transform this information into a segmentation of an object that matches those extreme points. We demonstrate the usefulness of this approach for guided segmentation (grabcut-style), interactive segmentation, video object segmentation, and dense segmentation annotation. We show that we obtain the most precise results to date, also with less user input, in an extensive and varied selection of benchmarks and datasets. All our models and code are publicly available on http://www.vision.ee.ethz.ch/~cvlsegmentation/dextr.
Cite
Text
Maninis et al. "Deep Extreme Cut: From Extreme Points to Object Segmentation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00071Markdown
[Maninis et al. "Deep Extreme Cut: From Extreme Points to Object Segmentation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/maninis2018cvpr-deep/) doi:10.1109/CVPR.2018.00071BibTeX
@inproceedings{maninis2018cvpr-deep,
title = {{Deep Extreme Cut: From Extreme Points to Object Segmentation}},
author = {Maninis, Kevis-Kokitsi and Caelles, Sergi and Pont-Tuset, Jordi and Van Gool, Luc},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2018},
doi = {10.1109/CVPR.2018.00071},
url = {https://mlanthology.org/cvpr/2018/maninis2018cvpr-deep/}
}