  Detection of out-of-distribution (OoD) samples in the context of image
classification has recently become an area of interest and active study, along
with the topic of uncertainty estimation, to which it is closely related. In
this paper we explore the task of OoD segmentation, which has been studied less
than its classification counterpart and presents additional challenges.
Segmentation is a dense prediction task for which the model's outcome for each
pixel depends on its surroundings. The receptive field and the reliance on
context play a role for distinguishing different classes and, correspondingly,
for spotting OoD entities. We introduce MOoSe, an efficient strategy to
leverage the various levels of context represented within semantic segmentation
models and show that even a simple aggregation of multi-scale representations
has consistently positive effects on OoD detection and uncertainty estimation.
