ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation (2401.08501v2)

Abstract: Uncertainty estimation is an essential and heavily-studied component for the reliable application of semantic segmentation methods. While various studies exist claiming methodological advances on the one hand, and successful application on the other hand, the field is currently hampered by a gap between theory and practice leaving fundamental questions unanswered: Can data-related and model-related uncertainty really be separated in practice? Which components of an uncertainty method are essential for real-world performance? Which uncertainty method works well for which application? In this work, we link this research gap to a lack of systematic and comprehensive evaluation of uncertainty methods. Specifically, we identify three key pitfalls in current literature and present an evaluation framework that bridges the research gap by providing 1) a controlled environment for studying data ambiguities as well as distribution shifts, 2) systematic ablations of relevant method components, and 3) test-beds for the five predominant uncertainty applications: OoD-detection, active learning, failure detection, calibration, and ambiguity modeling. Empirical results on simulated as well as real-world data demonstrate how the proposed framework is able to answer the predominant questions in the field revealing for instance that 1) separation of uncertainty types works on simulated data but does not necessarily translate to real-world data, 2) aggregation of scores is a crucial but currently neglected component of uncertainty methods, 3) While ensembles are performing most robustly across the different downstream tasks and settings, test-time augmentation often constitutes a light-weight alternative. Code is at: https://github.com/IML-DKFZ/values

• The paper presents a framework that evaluates individual and combined roles of segmentation backbone, prediction model, uncertainty measure, and aggregation strategy.
• The paper reveals that aggregating uncertainty estimates is critical in addressing data ambiguities and distribution shifts.
• The paper demonstrates that ensemble models and test-time augmentation provide reliable improvements in segmentation performance.

Understanding Uncertainty in Semantic Segmentation

Introducing a Better Evaluation Framework

The process of estimating uncertainty in semantic segmentation has progressed with several methodological claims, but there has been a disconnect between theory and practical application. A significant challenge is the ability to effectively separate data-related uncertainty (aleatoric uncertainty, AU) and model-related uncertainty (epistemic uncertainty, EU). A novel evaluation framework introduced in this paper systematically addresses these issues by creating controlled environments to analyze data ambiguities and distribution shifts.

Key Components and Validation

Researchers have developed the framework to scrutinize four critical components of uncertainty estimation:

1. The segmentation backbone
2. The prediction model
3. The uncertainty measure
4. The aggregation strategy

The framework allows evaluating these components individually and in combination, providing insights on how each contributes to the overall performance. This rigorous validation is essential to ensure that any improvements are not overshadowed by overlooked elements such as inadequate aggregation methods, which have been identified as a frequent issue in current research practices.

Deep Dive into Results

Extensive empirical studies utilizing both simulated and real-world datasets shed light on practical applications. The findings reveal three crucial takeaways:

• Real-world data complexities may not align with the theoretical separation of AU and EU.
• Aggregating uncertainty estimates is a vital yet underappreciated part of the process.
• Ensemble models rank as the most reliable across various tasks, with test-time augmentation presenting itself as a viable lightweight alternative.

Systematic ablations confirm that separations indeed can be beneficial, depending on the dataset and task at hand. Thus, separating the two types of uncertainties is not universally advantageous.

Moving Forward

Encouraging meticulous scrutiny within research, the framework sets a new standard for the evaluation of uncertainty methods. By delineating the connections to real-world efficacy, it steers future advancements towards practical significance rather than theoretical allure. Practitioners now have a valuable tool to optimize their decision-making process for uncertainty methods, fostering a more reliable deployment of segmentation systems in the field.