SharpContour: A Contour-based Boundary Refinement Approach for Efficient and Accurate Instance Segmentation

Chenming Zhu, Xuanye Zhang, Yanran Li, Liangdong Qiu, Kai Han, Xiaoguang Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Excellent performance has been achieved on instance segmentation but the quality on the boundary area remains unsatisfactory, which leads to a rising attention on boundary refinement. For practical use, an ideal post-processing refinement scheme are required to be accurate, generic and efficient. However, most of existing approaches propose pixel-wise refinement, which either introduce a massive computation cost or design specifically for different backbone models. Contour-based models are efficient and generic to be incorporated with any existing segmentation methods, but they often generate over-smoothed contour and tend to fail on corner areas. In this paper, we propose an efficient contour-based boundary refinement approach, named SharpContour, to tackle the segmentation of boundary area. We design a novel contour evolution process together with an Instance-aware Point Classifier. Our method deforms the contour iteratively by updating offsets in a discrete manner. Differing from existing contour evolution methods, SharpContour estimates each offset more independently so that it predicts much sharper and accurate contours. Notably, our method is generic to seamlessly work with diverse existing models with a small computational cost. Experiments show that SharpContour achieves competitive gains whilst preserving high efficiency.
Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages4382-4391
Number of pages10
ISBN (Electronic)978-1-6654-6946-3
ISBN (Print)978-1-6654-6947-0
DOIs
Publication statusPublished - 27 Sept 2022
EventIEEE/CVF Conference on Computer Vision and Pattern Recognition 2022
- New Orleans, United States
Duration: 19 Jun 202224 Jun 2022
https://cvpr2022.thecvf.com/

Publication series

NameIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherIEEE
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

ConferenceIEEE/CVF Conference on Computer Vision and Pattern Recognition 2022
Abbreviated titleCVPR 2022
Country/TerritoryUnited States
CityNew Orleans
Period19/06/2224/06/22
Internet address

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