Masked Gamma-SSL: Learning Uncertainty Estimation via Masked Image
  Modeling
David S. W. Williams Matthew Gadd Paul Newman Daniele De Martini
This work proposes a semantic segmentation network that produces high-quality
uncertainty estimates in a single forward pass. We exploit general
representations from foundation models and unlabelled datasets through a Masked
Image Modeling (MIM) approach, which is robust to augmentation hyper-parameters
and simpler than previous techniques. For neural networks used in
safety-critical applications, bias in the training data can lead to errors;
therefore it is crucial to understand a network's limitations at run time and
act accordingly. To this end, we test our proposed method on a number of test
domains including the SAX Segmentation benchmark, which includes labelled test
data from dense urban, rural and off-road driving domains. The proposed method
consistently outperforms uncertainty estimation and Out-of-Distribution (OoD)
techniques on this difficult benchmark.