Hyperbolic Image Segmentation

IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings, pp. 4443–4452

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.

Authors 5

1. Mina Ghadimi Atigh

   University of Amsterdam

   Affiliation as printed

   University of Amsterdam

2. Julian Schoep

   University of Amsterdam

   Affiliation as printed

   University of Amsterdam

3. Erman Acar Aachen

   Leiden University · Vrije Universiteit Amsterdam

   Affiliation as printed

   Leiden University, Vrije Universiteit Amsterdam

4. Nanne van Noord

   University of Amsterdam

   Affiliation as printed

   University of Amsterdam

5. Pascal Mettes

   University of Amsterdam

   Affiliation as printed

   University of Amsterdam