  Deep learning models for semantic segmentation are prone to poor performance
in real-world applications due to the highly challenging nature of the task.
Model uncertainty quantification (UQ) is one way to address this issue of lack
of model trustworthiness by enabling the practitioner to know how much to trust
a segmentation output. Current UQ methods in this application domain are mainly
restricted to Bayesian based methods which are computationally expensive and
are only able to extract central moments of uncertainty thereby limiting the
quality of their uncertainty estimates. We present a simple framework for
high-resolution predictive uncertainty quantification of semantic segmentation
models that leverages a multi-moment functional definition of uncertainty
associated with the model's feature space in the reproducing kernel Hilbert
space (RKHS). The multiple uncertainty functionals extracted from this
framework are defined by the local density dynamics of the model's feature
space and hence automatically align themselves at the tail-regions of the
intrinsic probability density function of the feature space (where uncertainty
is the highest) in such a way that the successively higher order moments
quantify the more uncertain regions. This leads to a significantly more
accurate view of model uncertainty than conventional Bayesian methods.
Moreover, the extraction of such moments is done in a single-shot computation
making it much faster than Bayesian and ensemble approaches (that involve a
high number of forward stochastic passes of the model to quantify its
uncertainty). We demonstrate these advantages through experimental evaluations
of our framework implemented over four different state-of-the-art model
architectures that are trained and evaluated on two benchmark road-scene
segmentation datasets (Camvid and Cityscapes).
