CVPR 2022poster114 citations
Hyperbolic Image Segmentation
Mina Ghadimi Atigh, Julian Schoep, Erman Acar, Nanne van Noord, Pascal Mettes
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.
BibTeX
@inproceedings{cvpr2022_hyperbolicimages,
title = {Hyperbolic Image Segmentation},
author = {Mina Ghadimi Atigh and Julian Schoep and Erman Acar and Nanne van Noord and Pascal Mettes},
booktitle = {CVPR 2022},
year = {2022}
}