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arXiv AI

A Critical Synthesis of Uncertainty Quantification and Foundation Models for Semantic Segmentation

The paper presents the first systematic evaluation of uncertainty quantification (UQ) methods applied to a foundation model for semantic segmentation. By fine‑tuning a lightweight DPT decoder on the pretrained SAM2 encoder, the authors benchmark four UQ approaches—Monte Carlo Dropout, Deep Sub‑Ensemble, Test‑Time Augmentation, and Evidential Deep Learning—across Cityscapes, NYUv2, and two out‑of‑domain settings, comparing segmentation accuracy, calibration, uncertainty quality, and inference time. The results reveal clear trade‑offs between predictive performance, reliability, and computational cost, underscoring both the promise and current limitations of uncertainty‑aware foundation models for real‑world deployment.

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Hugging Face Trending Papers
Aug 19

A Critical Synthesis of Uncertainty Quantification and Foundation Models for Semantic Segmentation

The paper presents the first systematic evaluation of uncertainty quantification (UQ) methods applied to a foundation model for semantic segmentation. By fine‑tuning a lightweight DPT decoder on the pretrained SAM2 encoder, the authors benchmark four UQ approaches—Monte Carlo Dropout, Deep Sub‑Ensemble, Test‑Time Augmentation, and Evidential Deep Learning—across Cityscapes, NYUv2, and two out‑of‑domain settings. The study compares segmentation accuracy, calibration, uncertainty quality, and inference time, revealing trade‑offs between predictive performance, reliability, and computational cost.

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arXiv AI
Aug 20

The Impact of CutMix on Reliability and Robustness in Semantic Segmentation

The paper investigates how the CutMix data augmentation technique affects reliability and robustness in semantic segmentation. It evaluates two architectures—CNN-based DeepLabV3+ and transformer-based SegFormer—on both in-domain and out-of-domain data. Results show that CutMix has a minor effect on segmentation accuracy but consistently improves reliability, especially under distribution shifts, by enhancing calibration and uncertainty quality.

By Steven Landgraf, Markus Ulrich
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arXiv Machine Learning
2d ago

PRUE: A Practical Recipe for Field Boundary Segmentation at Scale

arXiv:2603.27101v2 Announce Type: replace-cross Abstract: Large-scale maps of field boundaries are essential for agricultural monitoring tasks. Existing deep learning approaches for satellite-based f...

By Gedeon Muhawenayo, Caleb Robinson, Subash Khanal, Zhanpei Fang, Isaac Corley, Alexander Wollam, Tianyi Gao, Leonard Strnad, Ryan Avery, Lyndon Estes, Ana M. T\'arano, Nathan Jacobs, Hannah Kerner
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arXiv Machine Learning
Aug 28

A Framework for Low-Effort Training Data Generation for Urban Semantic Segmentation

The paper introduces a framework that adapts a diffusion model to a target urban domain using only imperfect pseudo‑labels, enabling the generation of high‑fidelity, target‑aligned images from semantic maps of any synthetic dataset. By filtering poor generations, correcting image‑label misalignments, and standardising semantics, the method transforms low‑effort synthetic data into competitive real‑domain training sets. Experiments on five synthetic and two real datasets show up to +8.0 %pt mIoU improvement over state‑of‑the‑art translation methods, demonstrating that rapidly constructed synthetic datasets can match the performance of high‑effort, manually designed ones.

By Damjan Kal\v{s}an, Denis Zavadski, Tim K\"uchler, Haebom Lee, Stefan Roth, Carsten Rother
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