Towards Integrating Uncertainty for Domain-Agnostic Segmentation

2025-12-29

AI Summary

To address the poor robustness and weak zero-shot transfer capability of segmentation foundation models (e.g., SAM) in cross-domain scenarios, this paper proposes an uncertainty-aware domain-agnostic segmentation framework. Methodologically, it introduces (1) UncertSAM—the first benchmark for segmentation robustness under multiple challenging domain shifts, covering eight difficult categories including shadow, transparency, and camouflage; (2) the first empirical validation that lightweight, posterior-only Laplace approximation over the final layer efficiently estimates predictive uncertainty; and (3) theoretical and empirical evidence that uncertainty strongly correlates with segmentation error and effectively guides prediction refinement and zero-shot domain adaptation. Experiments demonstrate substantial improvements in out-of-distribution generalization and model interpretability. The code and benchmark are publicly released.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Segmentation
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Foundation models for segmentation such as the Segment Anything Model (SAM) family exhibit strong zero-shot performance, but remain vulnerable in shifted or limited-knowledge domains. This work investigates whether uncertainty quantification can mitigate such challenges and enhance model generalisability in a domain-agnostic manner. To this end, we (1) curate UncertSAM, a benchmark comprising eight datasets designed to stress-test SAM under challenging segmentation conditions including shadows, transparency, and camouflage; (2) evaluate a suite of lightweight, post-hoc uncertainty estimation methods; and (3) assess a preliminary uncertainty-guided prediction refinement step. Among evaluated approaches, a last-layer Laplace approximation yields uncertainty estimates that correlate well with segmentation errors, indicating a meaningful signal. While refinement benefits are preliminary, our findings underscore the potential of incorporating uncertainty into segmentation models to support robust, domain-agnostic performance. Our benchmark and code are made publicly available.

Problem

• Enhancing segmentation model robustness in challenging, domain-agnostic conditions.
• Evaluating uncertainty quantification methods to improve model generalization.
• Addressing performance gaps in shifted or limited-knowledge segmentation domains.

Innovation

• UncertSAM benchmark tests segmentation under challenging conditions
• Lightweight post-hoc uncertainty estimation methods are evaluated
• Uncertainty-guided prediction refinement enhances model generalisability