Identifiable Object Representations under Spatial Ambiguities

Avinash Kori; Francesca Toni; Ben Glocker

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arXiv · 2506.07806

Identifiable Object Representations under Spatial Ambiguities

Avinash Kori·Francesca Toni·Ben Glocker

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Abstract

Modular object-centric representations are essential for *human-like reasoning* but are challenging to obtain under spatial ambiguities, *e.g. due to occlusions and view ambiguities*. However, addressing challenges presents both theoretical and practical difficulties. We introduce a novel multi-view probabilistic approach that aggregates view-specific slots to capture *invariant content* information while simultaneously learning disentangled global *viewpoint-level* information. Unlike prior single-view methods, our approach resolves spatial ambiguities, provides theoretical guarantees for identifiability, and requires *no viewpoint annotations*. Extensive experiments on standard benchmarks and novel complex datasets validate our method's robustness and scalability.

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Avinash Kori, Francesca Toni, Ben Glocker. 2025-06-09. Identifiable Object Representations under Spatial Ambiguities. https://arxiv.org/abs/2506.07806

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