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 language | English |
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Title of host publication | Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 4382-4391 |
Number of pages | 10 |
ISBN (Electronic) | 978-1-6654-6946-3 |
ISBN (Print) | 978-1-6654-6947-0 |
DOIs | |
Publication status | Published - 27 Sept 2022 |
Event | IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022 - New Orleans, United States Duration: 19 Jun 2022 → 24 Jun 2022 https://cvpr2022.thecvf.com/ |
Publication series
Name | IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
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Publisher | IEEE |
ISSN (Print) | 1063-6919 |
ISSN (Electronic) | 2575-7075 |
Conference
Conference | IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022 |
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Abbreviated title | CVPR 2022 |
Country/Territory | United States |
City | New Orleans |
Period | 19/06/22 → 24/06/22 |
Internet address |