Mitigating Distributional Shift in Semantic Segmentation via Uncertainty
  Estimation from Unlabelled Data
David S. W. Williams Daniele De Martini Matthew Gadd Paul Newman
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 (AVs). This work 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
labelled datasets, we use easy-to-obtain, uncurated and unlabelled data to
learn to perform uncertainty estimation by selectively enforcing consistency
over data augmentation. To this end, a novel segmentation benchmark based on
the SAX Dataset is used, which includes labelled test data spanning three
autonomous-driving domains, ranging in appearance from dense urban to off-road.
The proposed method, named Gamma-SSL, consistently outperforms uncertainty
estimation and Out-of-Distribution (OoD) techniques on this difficult benchmark
- by up to 10.7% in area under the receiver operating characteristic (ROC)
curve and 19.2% in area under the precision-recall (PR) curve in the most
challenging of the three scenarios.