  In recent years, deep neural networks have defined the state-of-the-art in
semantic segmentation where their predictions are constrained to a predefined
set of semantic classes. They are to be deployed in applications such as
automated driving, although their categorically confined expressive power runs
contrary to such open world scenarios. Thus, the detection and segmentation of
objects from outside their predefined semantic space, i.e., out-of-distribution
(OoD) objects, is of highest interest. Since uncertainty estimation methods
like softmax entropy or Bayesian models are sensitive to erroneous predictions,
these methods are a natural baseline for OoD detection. Here, we present a
method for obtaining uncertainty scores from pixel-wise loss gradients which
can be computed efficiently during inference. Our approach is simple to
implement for a large class of models, does not require any additional training
or auxiliary data and can be readily used on pre-trained segmentation models.
Our experiments show the ability of our method to identify wrong pixel
classifications and to estimate prediction quality at negligible computational
overhead. In particular, we observe superior performance in terms of OoD
segmentation to comparable baselines on the SegmentMeIfYouCan benchmark,
clearly outperforming other methods.
