Author

Fabian Hüger

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Preprint Sep 2026

Combining Foundation Model Confidence and Monocular Depth for Training-Free Out-of-Distribution Segmentation

Autonomous vehicles operating in open-world scenarios are inevitably confronted with previously unknown objects, such as exotic animals or loose cargo. The reliable detection and segmentation of these out-of-distribution (OOD) objects is therefore crucial for a safe understanding of the environment and decision-making....

Serin Varghese, Fabian Hüger, Kira Maag · 0 citations

Preprint Sep 2026

Diffuse2Seg: Diffusion Models Can Segment Anything Without Supervision

Open-world entity segmentation aims to predict masks for arbitrary objects across domains and at multiple granularities, from parts to whole objects. In this setting, SAM sets a strong standard: trained on SA-1B, comprising 11M images and over 1B carefully annotated masks, it achieves remarkable zero-shot performance....

Christoph Hümmer, Joachim Sicking, Fabian Hüger et al. · 0 citations