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

• URL: http://arxiv.org/abs/2512.11373v1
• Date: Fri, 12 Dec 2025 08:36:59 GMT
• Title: Out-of-Distribution Segmentation via Wasserstein-Based Evidential Uncertainty
• Authors: Arnold Brosch, Abdelrahman Eldesokey, Michael Felsberg, Kira Maag,
• Abstract summary: We present an evidence segmentation framework using a Wasserstein loss, which captures distributional distances while respecting the probability simplex geometry.<n>Our approach leads to improved OOD segmentation performance compared to uncertainty-based approaches.
• Score: 22.45768202733885
• License: http://creativecommons.org/licenses/by/4.0/
• 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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