Knowing when a trained segmentation model is encountering data that is different to its training data is important. Understanding and mitigating the effects of this play an important part in their application from a performance and assurance perspective—this being a safety concern in applications such as autonomous vehicles. This article presents a segmentation network that can detect errors caused by challenging test domains without any additional annotation in a single forward pass. As annotation costs limit the diversity of labeled datasets, we use easy-to-obtain, uncurated and unlabeled data to learn to perform uncertainty estimation by selectively enforcing consistency over data augmentation. To this end, a novel segmentation benchmark based on the sense-assess-eXplain (SAX) is used, which includes labeled test data spanning three autonomous-driving domains, ranging in appearance from dense urban to off-road. The proposed method, named $\mathrm{\gamma }\text{-}\text{SSL}$ , consistently outperforms uncertainty estimation and out-of-distribution techniques on this difficult benchmark—by up to 10.7% in area under the receiver operating characteristic curve and 19.2% in area under the precision-recall curve in the most challenging of the three scenarios.

中文翻译：

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通过未标记数据的不确定性估计来减轻语义分割中的分布变化


了解经过训练的分割模型何时遇到与其训练数据不同的数据非常重要。从性能和保证的角度来看，理解和减轻这种影响在其应用中发挥着重要作用——这是自动驾驶汽车等应用中的一个安全问题。本文提出了一种分段网络，可以检测由具有挑战性的测试域引起的错误，而无需在单次前向传递中添加任何附加注释。由于注释成本限制了标记数据集的多样性，因此我们使用易于获取、未经整理和未标记的数据来学习通过有选择地强制数据增强的一致性来执行不确定性估计。为此，使用了基于 sense-assess-eXplain (SAX) 的新颖分割基准，其中包括跨越三个自动驾驶领域的标记测试数据，范围从密集的城市到越野。所提出的方法名为 $\mathrm{\gamma }\text{-}\text{SSL}$ ，在这一困难的基准测试中始终优于不确定性估计和分布外技术，在接收器下方的区域中性能提高高达 10.7%在三种场景中最具挑战性的情况下，操作特性曲线和精确召回曲线下面积为 19.2%。