3 papers
cs.CV2026
A Probabilistic Perspective on Wasserstein-Based Evidential Uncertainty for Out-of-Distribution Segmentation
Arnold Brosch, Abdelrahman Eldesokey, Michael Felsberg +1
Semantic segmentation networks operate on a fixed set of classes and therefore fail when out-of-distribution (OOD) objects appear during deployment, a critical limitation for safet…
cs.CV2025
Out-of-Distribution Segmentation via Wasserstein-Based Evidential Uncertainty
Arnold Brosch, Abdelrahman Eldesokey, Michael Felsberg +1
Deep neural networks achieve superior performance in semantic segmentation, but are limited to a predefined set of classes, which leads to failures when they encounter unknown obje…
cs.CV2025
Dance Style Classification using Laban-Inspired and Frequency-Domain Motion Features
Ben Hamscher, Arnold Brosch, Nicolas Binninger +2
Dance is an essential component of human culture and serves as a tool for conveying emotions and telling stories. Identifying and distinguishing dance genres based on motion data i…