  Recent semantic segmentation models accurately classify test-time examples
that are similar to a training dataset distribution. However, their
discriminative closed-set approach is not robust in practical data setups with
distributional shifts and out-of-distribution (OOD) classes. As a result, the
predicted probabilities can be very imprecise when used as confidence scores at
test time. To address this, we propose a generative model for concurrent
in-distribution misclassification (IDM) and OOD detection that relies on a
normalizing flow framework. The proposed flow-based detector with an
energy-based inputs (FlowEneDet) can extend previously deployed segmentation
models without their time-consuming retraining. Our FlowEneDet results in a
low-complexity architecture with marginal increase in the memory footprint.
FlowEneDet achieves promising results on Cityscapes, Cityscapes-C, FishyScapes
and SegmentMeIfYouCan benchmarks in IDM/OOD detection when applied to
pretrained DeepLabV3+ and SegFormer semantic segmentation models.
