Uncertainty for Domain-Agnostic Segmentation
Published:Dec 29, 2025 12:46
•1 min read
•ArXiv
Analysis
This paper addresses a critical limitation of foundation models like SAM: their vulnerability in challenging domains. By exploring uncertainty quantification, the authors aim to improve the robustness and generalizability of segmentation models. The creation of a new benchmark (UncertSAM) and the evaluation of post-hoc uncertainty estimation methods are significant contributions. The findings suggest that uncertainty estimation can provide a meaningful signal for identifying segmentation errors, paving the way for more reliable and domain-agnostic performance.
Key Takeaways
- •Investigates the use of uncertainty quantification to improve the robustness of segmentation models.
- •Introduces UncertSAM, a new benchmark for evaluating segmentation models under challenging conditions.
- •Evaluates post-hoc uncertainty estimation methods.
- •Finds that a last-layer Laplace approximation provides a meaningful uncertainty signal.
- •Highlights the potential of uncertainty-guided prediction refinement.
Reference
“A last-layer Laplace approximation yields uncertainty estimates that correlate well with segmentation errors, indicating a meaningful signal.”