Fast User-Guided Video Object Segmentation by Interaction-And-Propagation Networks

Seoung Wug Oh, Joon-Young Lee, Ning Xu, Seon Joo Kim; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 5247-5256

Abstract


We present a deep learning method for the interactive video object segmentation. Our method is built upon two core operations, interaction and propagation, and each operation is conducted by Convolutional Neural Networks. The two networks are connected both internally and externally so that the networks are trained jointly and interact with each other to solve the complex video object segmentation problem. We propose a new multi-round training scheme for the interactive video object segmentation so that the networks can learn how to understand the user's intention and update incorrect estimations during the training. At the testing time, our method produces high-quality results and also runs fast enough to work with users interactively. We evaluated the proposed method quantitatively on the interactive track benchmark at the DAVIS Challenge 2018. We outperformed other competing methods by a significant margin in both the speed and the accuracy. We also demonstrated that our method works well with real user interactions.

Related Material


[pdf]
[bibtex]
@InProceedings{Oh_2019_CVPR,
author = {Oh, Seoung Wug and Lee, Joon-Young and Xu, Ning and Kim, Seon Joo},
title = {Fast User-Guided Video Object Segmentation by Interaction-And-Propagation Networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}