arXiv AI October 27, 2026 By Despoina Touska, Bastiaan Onne Fagginger Auer, Alexandru Onose, Tejaswi Kasarla, Luis Armando P\'erez Rey, Maximilian Lipp, Lyubov Amitonova, Martin R. Oswald, Pascal Cerfontaine

OrthoRF: Exploring Orthogonality in Object-Centric Representations

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OrthoRF introduces orthogonality into Rotating Features (RF) by applying an inner-product loss and architectural changes, improving phase alignment and grouping reliability. The method outperforms or matches existing unsupervised object discovery models, especially in challenging scenarios such as overlapping objects, noise, and out-of-distribution tests. Additionally, OrthoRF can recover occluded object parts without requiring post-hoc clustering, producing more interpretable representations.

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