29 citations · 86 across the 22 of their papers we have counts for
5 papers · 1 filter
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…
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…
Transferring Styles for Reduced Texture Bias and Improved Robustness in Semantic Segmentation Networks
Ben Hamscher, Edgar Heinert, Annika Mütze +2
Recent research has investigated the shape and texture biases of deep neural networks (DNNs) in image classification which influence their generalization capabilities and robustnes…
VideoGAN-based Trajectory Proposal for Automated Vehicles
Annajoyce Mariani, Kira Maag, Hanno Gottschalk
Being able to generate realistic trajectory options is at the core of increasing the degree of automation of road vehicles. While model-driven, rule-based, and classical learning-b…
Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection
Tobias J. Riedlinger, Kira Maag, Hanno Gottschalk
Deep neural networks have set the state-of-the-art in computer vision tasks such as bounding box detection and semantic segmentation. Object detectors and segmentation models assig…