  In this work, we train a network to simultaneously perform segmentation and
pixel-wise Out-of-Distribution (OoD) detection, such that the segmentation of
unknown regions of scenes can be rejected. This is made possible by leveraging
an OoD dataset with a novel contrastive objective and data augmentation scheme.
By combining data including unknown classes in the training data, a more robust
feature representation can be learned with known classes represented distinctly
from those unknown. When presented with unknown classes or conditions, many
current approaches for segmentation frequently exhibit high confidence in their
inaccurate segmentations and cannot be trusted in many operational
environments. We validate our system on a real-world dataset of unusual driving
scenes, and show that by selectively segmenting scenes based on what is
predicted as OoD, we can increase the segmentation accuracy by an IoU of 0.2
with respect to alternative techniques.
