A Hyperbolic Perspective on Hierarchical Structure in Object-Centric Scene Representations
Slot attention has emerged as a powerful framework for unsupervised object-centric learning, decomposing visual scenes into a small set of compact vector representations called \emph{slots}, each capturing a distinct region or object. However, these slots are learned in Euclidean space, which provides no geometric inductive bias for the hierarchical relationships that naturally structure visual scenes. In this work, we propose a simple post-hoc pipeline to project Euclidean slot embeddings onto the Lorentz hyperboloid of hyperbolic space, without modifying the underlying training pipeline. We construct five-level visual hierarchies directly from slot attention masks and analyse whether hy...
| Authors | Neelu Madan, Àlex Pujol, Andreas Møgelmose et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 March 2026 |
| Last updated at source | 30 March 2026 |
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