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ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation
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Proposes ValUES, a framework and empirical study for systematically validating uncertainty estimation methods in semantic segmentation, using controlled aleatoric/epistemic shifts on toy, LIDC-IDRI and GTA5/Cityscapes data across five downstream tasks.
Problem
Uncertainty methods for semantic segmentation are claimed to separate aleatoric and epistemic uncertainty and to be useful for downstream tasks, but such claims are rarely validated systematically; components (aggregation, prediction model, measure) and downstream tasks are rarely evaluated jointly.
Approach
A framework that decomposes uncertainty methods into four components (backbone, prediction model, uncertainty measure, aggregation strategy), induces controlled aleatoric and epistemic uncertainty in data, and tests each component across five downstream tasks (OoD detection, active learning, failure detection, calibration, ambiguity modeling).
Key results
- Ensembles are the most robust prediction model across downstream tasks, with TTA often a light-weight alternative; TTA is found to model epistemic rather than aleatoric uncertainty.
- Separation of AU and EU works on the toy dataset but does not consistently translate to real-world LIDC and GTA5/CS data.
- Aggregation strategy has a substantial, dataset-dependent influence on downstream performance, with standard deviations across components often exceeding differences.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use AU-measures instead of EU-measures | Uncertainty estimation · Toy dataset | NCC | Helped |
| Use AU-measures instead of EU-measures | Uncertainty estimation · LIDC | NCC | No clear effect |
| Use AU-measures instead of EU-measures | Uncertainty estimation · GTA5/Cityscapes | NCC | Helped |
| Use EU-measures instead of AU- or PU-measures | Uncertainty estimation · Toy, LIDC, and GTA5/Cityscapes | AUROC | Helped |
| Use test-time augmentation (TTA) to estimate EU | Uncertainty estimation · Separation study | AUROC | Helped |
| Use EU-measures instead of PU-measures | Uncertainty estimation · Active learning, except LIDC MAL | AL improvement | Helped |
| Use ensembles | Uncertainty estimation · OoD detection across datasets | AUROC | Helped |
| Use TTA instead of other prediction models | Uncertainty estimation · OoD detection across datasets | AUROC | Helped |
| Use ensembles | Uncertainty estimation · Failure detection | AURC | Helped |
| Use PU-measures instead of other uncertainty types | Uncertainty estimation · Calibration across datasets | ACE | Helped |
| Use AU-measures instead of other uncertainty types | Uncertainty estimation · Ambiguity modeling across datasets | NCC | Helped |
| Use SSNs instead of other prediction models | Uncertainty estimation · Ambiguity modeling across datasets | NCC | Helped |
Surprising findings
- Surveyed claims that TTA captures aleatoric uncertainty are contradicted; TTA is found most suited for modeling epistemic uncertainty.
- Surpassing the random active learning baseline is challenging, with only marginal improvements on LIDC MAL and GTA5/CS.
Limitations
- Only one backbone per dataset (3D U-Net, HRNet) is fixed; backbone (C0) is not varied.
- Real-world results show inconsistent separation, and aggregation strategies are dataset-dependent, so conclusions may not generalize.
- Hyperparameters were not extensively searched; standard values were used.
Experimental setup
Datasets: LIDC-IDRI GTA5 Cityscapes Synthetic 3D toy dataset (spheres and cubes)
Benchmarks: Toy dataset LIDC-IDRI (texture and malignancy shifts) GTA5/Cityscapes
Metrics: NCC AUROC AURC E-AURC ACE Dice GED AL improvement over random
Baselines: deterministic softmax MC-Dropout (test-time dropout) deep ensemble test-time augmentation stochastic segmentation network
Builds on: Bayesian deep learning uncertainty decomposition stochastic segmentation networks generalized energy distance 3D U-Net
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