Out-of-Distribution Segmentation via Wasserstein-Based Evidential Uncertainty
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912759844700160 |
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| author | Brosch, Arnold Eldesokey, Abdelrahman Felsberg, Michael Maag, Kira |
| author_facet | Brosch, Arnold Eldesokey, Abdelrahman Felsberg, Michael Maag, Kira |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11373 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Out-of-Distribution Segmentation via Wasserstein-Based Evidential Uncertainty Brosch, Arnold Eldesokey, Abdelrahman Felsberg, Michael Maag, Kira Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Out-of-Distribution Segmentation via Wasserstein-Based Evidential Uncertainty |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2512.11373 |