  Most dense recognition approaches bring a separate decision in each
particular pixel. These approaches deliver competitive performance in usual
closed-set setups. However, important applications in the wild typically
require strong performance in presence of outliers. We show that this demanding
setup greatly benefit from mask-level predictions, even in the case of
non-finetuned baseline models. Moreover, we propose an alternative formulation
of dense recognition uncertainty that effectively reduces false positive
responses at semantic borders. The proposed formulation produces a further
improvement over a very strong baseline and sets the new state of the art in
outlier-aware semantic segmentation with and without training on negative data.
Our contributions also lead to performance improvement in a recent panoptic
setup. In-depth experiments confirm that our approach succeeds due to implicit
aggregation of pixel-level cues into mask-level predictions.
