@inproceedings{494402c1299b47cf8d2e9c356679d29f,
title = "MaskPlus: Improving mask generation for instance segmentation",
abstract = "Instance segmentation is a promising yet challenging topic in computer vision. Recent approaches such as Mask R-CNN typically divide this problem into two parts - a detection component and a mask generation branch, and mostly focus on the improvement of the detection part. In this paper, we present an approach that extends Mask R-CNN with five novel techniques for improving the mask generation branch and reducing the conflicts between the mask branch and the detection component in training. These five techniques are independent to each other and can be flexibly utilized in building various instance segmentation architectures for increasing the overall accuracy. We demonstrate the effectiveness of our approach with tests on the COCO dataset.",
author = "Shichao Xu and Shuyue Lan and Qi Zhu",
note = "Funding Information: We gratefully acknowledge the support from the US National Science Foundation awards 1724341 and 1834701. Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020 ; Conference date: 01-03-2020 Through 05-03-2020",
year = "2020",
month = mar,
doi = "10.1109/WACV45572.2020.9093379",
language = "English (US)",
series = "Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2019--2027",
booktitle = "Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020",
address = "United States",
}