Identifiable Object Representations under Spatial Ambiguities

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.

@article{kori2025_2506.07806,
  title={ Identifiable Object Representations under Spatial Ambiguities },
  author={ Avinash Kori and Francesca Toni and Ben Glocker },
  journal={arXiv preprint arXiv:2506.07806},
  year={ 2025 }
}