A Hyperbolic Perspective on Hierarchical Structure in Object-Centric Scene Representations

ArXi:2603.14022v1 Announce Type: new 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.