A Critical Synthesis of Uncertainty Quantification and Foundation Models for Semantic Segmentation
Foundation models are increasingly breaking what seemed to be impossible not long ago by enabling unprecedented accuracy and cross-domain generalization. Yet their lack of interpretability, tendency to be overconfident, and sensitivity to real-world domain shifts pose critical challenges for safety- and mission-critical applications. Uncertainty quantification (UQ) offers a principled way to address these issues, but its integration into segmentation foundation models has yet to be explored. In this paper we present the first systematic evaluation of UQ methods applied to a foundation model for semantic segmentation. We fine-tune a lightweight DPT decoder on top of the pretrained SAM2 enc...
| Authors | Steven Landgraf, Joceline Hinz, Markus Ulrich |
|---|---|
| Primary category | cs.CV |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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