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.
Original language | English (US) |
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Title of host publication | Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2019-2027 |
Number of pages | 9 |
ISBN (Electronic) | 9781728165530 |
DOIs | |
State | Published - Mar 2020 |
Event | 2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020 - Snowmass Village, United States Duration: Mar 1 2020 → Mar 5 2020 |
Publication series
Name | Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 |
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Conference
Conference | 2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020 |
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Country/Territory | United States |
City | Snowmass Village |
Period | 3/1/20 → 3/5/20 |
Funding
We gratefully acknowledge the support from the US National Science Foundation awards 1724341 and 1834701.
ASJC Scopus subject areas
- Computer Science Applications
- Computer Vision and Pattern Recognition