Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation From Unlabeled Data

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.