  Recent works on predictive uncertainty estimation have shown promising
results on Out-Of-Distribution (OOD) detection for semantic segmentation.
However, these methods struggle to precisely locate the point of interest in
the image, i.e, the anomaly. This limitation is due to the difficulty of
finegrained prediction at the pixel level. To address this issue, we build upon
the recent ObsNet approach by providing object instance knowledge to the
observer. We extend ObsNet by harnessing an instance-wise mask prediction. We
use an additional, class agnostic, object detector to filter and aggregate
observer predictions. Finally, we predict an unique anomaly score for each
instance in the image. We show that our proposed method accurately disentangles
in-distribution objects from OOD objects on three datasets.
