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.
Abstract (translated)
深度神经网络在语义分割方面表现出色，但它们仅限于预定义的一组类别，这导致在遇到开放世界场景中的未知对象时会出现失败。识别和分割这些分布外（OOD）的对象对于自动驾驶等安全关键应用至关重要。在这项工作中，我们提出了一种基于 Wasserstein 损失的证据分割框架，该框架能够捕捉分布距离并尊重概率单纯形几何结构。结合 Kullback-Leibler 正则化和 Dice 结构一致性术语，我们的方法在 OOD 分割性能方面优于不确定性基线方法。
URL
https://arxiv.org/abs/2512.11373
