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

Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning