Instance-wise Uncertainty for Class Imbalance in Semantic Segmentation

TL;DR

This paper introduces a novel training method for semantic segmentation that uses instance-wise uncertainty masks to improve minority class performance and robustness, addressing class imbalance and uncertainty estimation issues.

Contribution

The paper presents a new training approach leveraging ensemble-based uncertainty masks tailored for semantic segmentation, enhancing minority class accuracy and model robustness.

Findings

Improved performance on minority classes.

Enhanced robustness to domain shifts.

Better uncertainty estimation in segmentation tasks.

Abstract

Semantic segmentation is a fundamental computer vision task with a vast number of applications. State of the art methods increasingly rely on deep learning models, known to incorrectly estimate uncertainty and being overconfident in predictions, especially in data not seen during training. This is particularly problematic in semantic segmentation due to inherent class imbalance. Popular uncertainty quantification approaches are task-agnostic and fail to leverage spatial pixel correlations in uncertainty estimates, crucial in this task. In this work, a novel training methodology specifically designed for semantic segmentation is presented. Training samples are weighted by instance-wise uncertainty masks computed by an ensemble. This is shown to increase performance on minority classes, boost model generalization and robustness to domain-shift when compared to using the inverse of class proportions or no class weights at all. This method addresses the challenges of class imbalance and uncertainty estimation in semantic segmentation, potentially enhancing model performance and reliability across various applications.