New Lorentz Framework for Semantic Segmentation in Hyperbolic Space
A new framework for semantic segmentation in hyperbolic space has been developed, utilizing the Lorentz model instead of the less effective Poincaré ball model. This architecture-agnostic method facilitates stable and efficient optimization without the need for Riemannian optimizers, allowing seamless integration with current Euclidean architectures. The technique incorporates text embeddings alongside semantic and visual cues to assist in creating hierarchical pixel-level representations within Lorentz space. In addition to segmentation, the framework offers uncertainty estimation, confidence maps, boundary detection, hierarchical and text-based retrieval, and zero-shot capabilities, achieving generalized flatter minima. This approach resolves issues of numerical instability and computational challenges found in earlier hyperbolic segmentation techniques. The research was published on arXiv with the identifier 2604.16836v1.
Key facts
- Semantic segmentation in hyperbolic space enables compact modeling of hierarchical structure with inherent uncertainty quantification
- Prior approaches predominantly rely on the Poincaré ball model which suffers from numerical instability, optimization, and computational challenges
- A novel, tractable, architecture-agnostic semantic segmentation framework in the hyperbolic Lorentz model has been proposed
- The framework works for both pixel-wise and mask classification approaches
- Text embeddings with semantic and visual cues guide hierarchical pixel-level representations in Lorentz space
- The approach enables stable and efficient optimization without requiring a Riemannian optimizer
- The framework easily integrates with existing Euclidean architectures
- Beyond segmentation, the approach yields free uncertainty estimation, confidence maps, boundary delineation, hierarchical and text-based retrieval, and zero-shot performance
Entities
Institutions
- arXiv