Hyperbolic Image Segmentation

Mina Ghadimi Atigh, Julian Schoep, Erman Acar, Nanne Van Noord, Pascal Mettes

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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 languageEnglish
Title of host publication2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Subtitle of host publication[Proceedings]
PublisherIEEE Computer Society
Pages4443-4452
Number of pages10
ISBN (Electronic)9781665469463
DOIs
Publication statusPublished - 27 Sept 2022
Event2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 - New Orleans, United States
Duration: 19 Jun 202224 Jun 2022

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2022-June
ISSN (Print)1063-6919

Conference

Conference2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
Country/TerritoryUnited States
CityNew Orleans
Period19/06/2224/06/22

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.

FundersFunder number
Ministerie van Onderwijs, Cultuur en Wetenschap
Not added024.004.022

    Keywords

    • grouping and shape analysis
    • Segmentation

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