# Implicit Background Estimation for Semantic Segmentation

## TL;DR

This paper proposes a method to improve the robustness of semantic segmentation models by correcting non-distinct mappings from the softmax function, with minimal performance impact.

## Contribution

It introduces a principled correction technique for softmax outputs that enhances model robustness without significant code modifications.

## Key findings

- Improved robustness in semantic segmentation models.
- Minimal performance degradation observed.
- Simple correction method applicable to existing models.

## Abstract

Scene understanding and semantic segmentation are at the core of many computer vision tasks, many of which, involve interacting with humans in potentially dangerous ways. It is therefore paramount that techniques for principled design of robust models be developed. In this paper, we provide analytic and empirical evidence that correcting potentially errant non-distinct mappings that result from the softmax function can result in improving robustness characteristics on a state-of-the-art semantic segmentation model with minimal impact to performance and minimal changes to the code base.