Abstract
For image segmentation, the current standard is to perform pixel-level optimization and inference in Euclidean output embedding spaces through linear hyperplanes. In this work, we show that hyperbolic manifolds provide a valuable alternative for image segmentation and propose a tractable formulation of hierarchical pixel-level classification in hyperbolic space. Hyperbolic Image Segmentation opens up new possibilities and practical benefits for segmentation, such as uncertainty estimation and boundary information for free, zero-label generalization, and increased performance in low-dimensional output embeddings.
Original language | English |
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Title of host publication | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
Subtitle of host publication | [Proceedings] |
Publisher | IEEE Computer Society |
Pages | 4443-4452 |
Number of pages | 10 |
ISBN (Electronic) | 9781665469463 |
DOIs | |
Publication status | Published - 27 Sept 2022 |
Event | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 - New Orleans, United States Duration: 19 Jun 2022 → 24 Jun 2022 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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Volume | 2022-June |
ISSN (Print) | 1063-6919 |
Conference
Conference | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 |
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Country/Territory | United States |
City | New Orleans |
Period | 19/06/22 → 24/06/22 |
Bibliographical note
Funding Information:Acknowledgments. Erman Acar is generously funded by the Hybrid Intelligence Project which is financed by the Dutch Ministry of Education, Culture and Science with project number 024.004.022.
Publisher Copyright:
© 2022 IEEE.
Funding
Acknowledgments. Erman Acar is generously funded by the Hybrid Intelligence Project which is financed by the Dutch Ministry of Education, Culture and Science with project number 024.004.022.
Funders | Funder number |
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Ministerie van Onderwijs, Cultuur en Wetenschap | |
Not added | 024.004.022 |
Keywords
- grouping and shape analysis
- Segmentation