  We present a simple and practical framework for anomaly segmentation called
Maskomaly. It builds upon mask-based standard semantic segmentation networks by
adding a simple inference-time post-processing step which leverages the raw
mask outputs of such networks. Maskomaly does not require additional training
and only adds a small computational overhead to inference. Most importantly, it
does not require anomalous data at training. We show top results for our method
on SMIYC, RoadAnomaly, and StreetHazards. On the most central benchmark, SMIYC,
Maskomaly outperforms all directly comparable approaches. Further, we introduce
a novel metric that benefits the development of robust anomaly segmentation
methods and demonstrate its informativeness on RoadAnomaly.
