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.
By Steven Landgraf, Joceline Hinz, Markus Ulrich
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
arXiv:2610.10116v1 Announce Type: new
Abstract: Semantic segmentation networks operate on a fixed set of classes and therefore fail when out-of-distribution (OOD) objects appear during deployment, a...
By Arnold Brosch, Abdelrahman Eldesokey, Michael Felsberg, Kira Maag
arXiv:2606. 31603v1 Announce Type: cross Abstract: Semantic segmentation models struggle with data sparsity and rare or visually diverse regions, e.
By Nikolai R\"ohrich, Julian Glei{\ss}ner, Ahmed H. A. Ibrahim, Silvan Mertes, Tobias Huber
arXiv:2608. 08308v1 Announce Type: cross Abstract: Modern vision systems must operate in "open-world" settings, where models must recognize known categories and detect unseen or anomalous content.
By Anastasios Romanos Varvarigos, Nikos Giakoumoglou, Tania Stathaki
arXiv:2509. 10334v2 Announce Type: replace-cross Abstract: Vision Transformers (ViTs) have recently achieved strong results in semantic segmentation, yet their deployment on resource-constrained devices remains limited due to their high memory footprint and computational cost.
By Jordan Sassoon, Michal Szczepanski, Martyna Poreba
The paper presents a hardware‑accelerated instance segmentation framework tailored for resource‑constrained lunar robotics, addressing low‑light perception, limited compute, and radiation‑induced hardware faults. It introduces Activation Variance Informative Sampling (AVIS), a label‑free calibration method that selects samples based on activation variance, and deploys a YOLO‑based model on a Deep Learning Processor Unit with architectural tweaks to reduce CPU fallback and ensure bounded latency. A software‑level criticality analysis estimates fault exposure, guiding mitigation that reduces global criticality by 31.7%, while AVIS with bias correction recovers 69.8% of quantization‑induced accuracy loss at 309 ms latency and 5.7 W power consumption.
By Siddhant Shete, Hilmi Dogu K\"uc\"uker, Udo Frese, Frank Kirchner
arXiv:2608. 04776v1 Announce Type: new Abstract: The ability to accurately assess and anticipate risks in safety-critical scenarios is crucial for autonomous driving systems.
By Yu Zhao, Jiangyu Pan, Tao Hu, Ming Yin, Fan Yang, Jiangfan Liu, Xiubo Liang
arXiv:2605.27136v2 Announce Type: replace
Abstract: Uncertainty quantification (UQ) remains a critical challenge in Large Vision Language Models (LVLMs) for reliable predictions and real-world deploy...
By Joseph Hoche, David Brellmann, Gianni Franchi
arXiv:2607. 20705v1 Announce Type: cross Abstract: Interactive image segmentation is critical for efficient image annotation; however, existing methods often require many corrective clicks or rely on passive refinement schemes that converge slowly.
By Elijah Danquah Darko, Min Xian, Terence Soule, Tiankai Yao, Matthew William Anderson
arXiv:2503.10685v3 Announce Type: replace
Abstract: Unsupervised Domain Adaptation (UDA) enables strong generalization from a labeled source domain to an unlabeled target domain, often with limited d...
By Brun\'o B. Englert, Gijs Dubbelman
arXiv:2406.09896v3 Announce Type: replace
Abstract: Achieving robust generalization across diverse data domains remains a significant challenge in computer vision. This challenge is important in safe...
By Brun\'o B. Englert, Fabrizio J. Piva, Tommie Kerssies, Daan de Geus, Gijs Dubbelman