Quantifying Model Uncertainty for Semantic Segmentation using Operators in the RKHS

TL;DR

This paper introduces a novel, efficient framework for quantifying model uncertainty in semantic segmentation using operators in the RKHS, providing more accurate and faster uncertainty estimates than traditional Bayesian methods.

Contribution

The authors propose a multi-moment functional approach in RKHS for high-resolution uncertainty quantification, outperforming Bayesian methods in accuracy and computational efficiency.

Findings

More accurate uncertainty estimates in semantic segmentation.

Single-shot computation of multiple uncertainty moments.

Demonstrated effectiveness across multiple models and datasets.

Abstract

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).