  Open-set segmentation can be conceived by complementing closed-set
classification with anomaly detection. Many of the existing dense anomaly
detectors operate through generative modelling of regular data or by
discriminating with respect to negative data. These two approaches optimize
different objectives and therefore exhibit different failure modes.
Consequently, we propose a novel anomaly score that fuses generative and
discriminative cues. Our score can be implemented by upgrading any closed-set
segmentation model with dense estimates of dataset posterior and unnormalized
data likelihood. The resulting dense hybrid open-set models require negative
training images that can be sampled from an auxiliary negative dataset, from a
jointly trained generative model, or from a mixture of both sources. We
evaluate our contributions on benchmarks for dense anomaly detection and
open-set segmentation. The experiments reveal strong open-set performance in
spite of negligible computational overhead.
