  Semantic segmentation is a scene understanding task at the heart of
safety-critical applications where robustness to corrupted inputs is essential.
Implicit Background Estimation (IBE) has demonstrated to be a promising
technique to improve the robustness to out-of-distribution inputs for semantic
segmentation models for little to no cost. In this paper, we provide analysis
comparing the structures learned as a result of optimization objectives that
use Softmax, IBE, and Sigmoid in order to improve understanding their
relationship to robustness. As a result of this analysis, we propose combining
Sigmoid with IBE (SCrIBE) to improve robustness. Finally, we demonstrate that
SCrIBE exhibits superior segmentation performance aggregated across all
corruptions and severity levels with a mIOU of 42.1 compared to both IBE 40.3
and the Softmax Baseline 37.5.
