  Outlier detection is an essential capability in safety-critical applications
of supervised visual recognition. Most of the existing methods deliver best
results by encouraging standard closed-set models to produce low-confidence
predictions in negative training data. However, that approach conflates
prediction uncertainty with recognition of the negative class. We therefore
reconsider direct prediction of K+1 logits that correspond to K groundtruth
classes and one outlier class. This setup allows us to formulate a novel
anomaly score as an ensemble of in-distribution uncertainty and the posterior
of the outlier class which we term negative objectness. Now outliers can be
independently detected due to i) high prediction uncertainty or ii) similarity
with negative data. We embed our method into a dense prediction architecture
with mask-level recognition over K+2 classes. The training procedure encourages
the novel K+2-th class to learn negative objectness at pasted negative
instances. Our models outperform the current state-of-the art on standard
benchmarks for image-wide and pixel-level outlier detection with and without
training on real negative data.
