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arXiv · 2203.05898

Hyperbolic Image Segmentation

Mina GhadimiAtigh·Julian Schoep·Erman Acar·Nanne van Noord·Pascal Mettes
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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.

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Mina GhadimiAtigh, Julian Schoep, Erman Acar, Nanne van Noord, Pascal Mettes. 2022-03-11. Hyperbolic Image Segmentation. https://arxiv.org/abs/2203.05898

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