Out-of-Distribution Segmentation via Wasserstein-Based Evidential Uncertainty

Arnold Brosch; Abdelrahman Eldesokey; Michael Felsberg; Kira Maag

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arXiv · 2512.11373

Out-of-Distribution Segmentation via Wasserstein-Based Evidential Uncertainty

Arnold Brosch·Abdelrahman Eldesokey·Michael Felsberg·Kira Maag

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Abstract

Deep neural networks achieve superior performance in semantic segmentation, but are limited to a predefined set of classes, which leads to failures when they encounter unknown objects in open-world scenarios. Recognizing and segmenting these out-of-distribution (OOD) objects is crucial for safety-critical applications such as automated driving. In this work, we present an evidence segmentation framework using a Wasserstein loss, which captures distributional distances while respecting the probability simplex geometry. Combined with Kullback-Leibler regularization and Dice structural consistency terms, our approach leads to improved OOD segmentation performance compared to uncertainty-based approaches.

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Arnold Brosch, Abdelrahman Eldesokey, Michael Felsberg, Kira Maag. 2025-12-12. Out-of-Distribution Segmentation via Wasserstein-Based Evidential Uncertainty. https://arxiv.org/abs/2512.11373

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