8 citations · 8 across the 7 of their papers we have counts for
7 papers
MMMS: Multi-Modal Multi-Surface Interactive Segmentation
Robin Schön, Julian Lorenz, Katja Ludwig +2
In this paper, we present a method to interactively create segmentation masks on the basis of user clicks. We pay particular attention to the segmentation of multiple surfaces that…
One-Shot Multi-Label Causal Discovery in High-Dimensional Event Sequences
Hugo Math, Robin Schön, Rainer Lienhart
Understanding causality in event sequences with thousands of sparse event types is critical in domains such as healthcare, cybersecurity, or vehicle diagnostics, yet current method…
CoPa-SG: Dense Scene Graphs with Parametric and Proto-Relations
Julian Lorenz, Mrunmai Phatak, Robin Schön +4
2D scene graphs provide a structural and explainable framework for scene understanding. However, current work still struggles with the lack of accurate scene graph data. To overcom…
Efficient 2D to Full 3D Human Pose Uplifting including Joint Rotations
Katja Ludwig, Yuliia Oksymets, Robin Schön +2
In sports analytics, accurately capturing both the 3D locations and rotations of body joints is essential for understanding an athlete's biomechanics. While Human Mesh Recovery (HM…
SkipClick: Combining Quick Responses and Low-Level Features for Interactive Segmentation in Winter Sports Contexts
Robin Schön, Julian Lorenz, Daniel Kienzle +1
In this paper, we present a novel architecture for interactive segmentation in winter sports contexts. The field of interactive segmentation deals with the prediction of high-quali…
Harnessing Event Sensory Data for Error Pattern Prediction in Vehicles: A Language Model Approach
Hugo Math, Rainer Lienhart, Robin Schön
In this paper, we draw an analogy between processing natural languages and processing multivariate event streams from vehicles in order to predict and $\textit{what…
Pseudo-Label Noise Suppression Techniques for Semi-Supervised Semantic Segmentation
Sebastian Scherer, Robin Schön, Rainer Lienhart
Semi-supervised learning (SSL) can reduce the need for large labelled datasets by incorporating unlabelled data into the training. This is particularly interesting for semantic seg…