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Contributed Talk
in
Affinity Workshop: Women in Machine Learning - Virtual

Contributed talk (Mina Ghadimi Atigh) - "Hyperbolic Image Segmentation"

Mina Ghadimi Atigh


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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