  A single unexpected object on the road can cause an accident or may lead to
injuries. To prevent this, we need a reliable mechanism for finding anomalous
objects on the road. This task, called anomaly segmentation, can be a stepping
stone to safe and reliable autonomous driving. Current approaches tackle
anomaly segmentation by assigning an anomaly score to each pixel and by
grouping anomalous regions using simple heuristics. However, pixel grouping is
a limiting factor when it comes to evaluating the segmentation performance of
individual anomalous objects. To address the issue of grouping multiple anomaly
instances into one, we propose an approach that produces accurate anomaly
instance masks. Our approach centers on an out-of-distribution segmentation
model for identifying uncertain regions and a strong generalist segmentation
model for anomaly instances segmentation. We investigate ways to use uncertain
regions to guide such a segmentation model to perform segmentation of anomalous
instances. By incorporating strong object priors from a generalist model we
additionally improve the per-pixel anomaly segmentation performance. Our
approach outperforms current pixel-level anomaly segmentation methods,
achieving an AP of 80.08% and 88.98% on the Fishyscapes Lost and Found and the
RoadAnomaly validation sets respectively. Project page:
https://vision.rwth-aachen.de/ugains
