2 citations · 2 across the 25 of their papers we have counts for
28 papers
A Multi-Source Ultrasound Benchmark Revealing the Limits of Contemporary Self-Supervised Anomaly Detection Methods
Marco Riedenauer, Daniel Kienzle, Pratik Mayekar +1
Self-supervised anomaly detection is a promising paradigm for medical ultrasound, as normal images are often easier to obtain than exhaustive annotations of all possible pathologie…
Event Detection in Table Tennis Videos using 2D Keypoints
Rainer Lienhart, Daniel Kienzle, Shin'ichi Satoh +1
This paper addresses the challenge of automatic, frame-accurate event detection in table tennis videos. Current methods for estimating 3d ball trajectories and ball spin typically…
SALI: Shot-Aware Late Interaction for Cross-Shot Relation Matching in Text-to-Video Retrieval using Film-Grammar Knowledge
Toya Oyama, Rainer Lienhart, Shin'ichi Satoh
Text-to-video retrieval usually represents a video clip by a single embedding. This embedding often loses important relations between people. E.g., an interaction "Anna confronts M…
Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark
Matthias Blaschke, Daniel Kienzle, Zsuzsanna Koczor-Benda +3
Generative molecular design is shaped by simple proxy benchmarks for drug-like properties and models pretrained on large pharmaceutical datasets. This combination yields strong ben…
TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videos
Nima Rahmanian, Daniel Kienzle, Thomas Gossard +3
We present TT4D, a large-scale, high-fidelity table tennis dataset. It provides hours of reconstructed singles and doubles gameplay from monocular broadcast videos, featurin…
Modernising Reinforcement Learning-Based Navigation for Embodied Semantic Scene Graph Generation
Roman Küble, Marco Hüller, Mrunmai Phatak +2
Semantic world models enable embodied agents to reason about objects, relations, and spatial context beyond purely geometric representations. In Organic Computing, such models are…
Multi-Agent Causal Reasoning System for Error Pattern Rule Automation in Vehicles
Hugo Math, Julian Lorenz, Stefan Oelsner +1
Modern vehicles generate thousands of different discrete events known as Diagnostic Trouble Codes (DTCs). Automotive manufacturers use Boolean combinations of these codes, called e…
Your Autoregressive Model Already Reveals the Causal Graph
Hugo Math, Rainer Lienhart
Autoregressive models trained via next-token prediction implicitly learn the conditional independence structure of their data-generating process. We exploit this observation to per…
Transforming Vehicle Diagnostics: A Multimodal Approach to Error Patterns Prediction
Hugo Math, Rainer Lienhart
Accurately diagnosing and predicting vehicle malfunctions is crucial for maintenance and safety in the automotive industry. While modern diagnostic systems primarily rely on sequen…
Uplifting Table Tennis: A Robust, Real-World Application for 3D Trajectory and Spin Estimation
Daniel Kienzle, Katja Ludwig, Julian Lorenz +2
Obtaining the precise 3D motion of a table tennis ball from standard monocular videos is a challenging problem, as existing methods trained on synthetic data struggle to generalize…
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…
Towards Practical Multi-label Causal Discovery in High-Dimensional Event Sequences via One-Shot Graph Aggregation
Hugo Math, Rainer Lienhart
Understanding causality in event sequences where outcome labels such as diseases or system failures arise from preceding events like symptoms or error codes is critical. Yet remain…
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…
HOIverse: A Synthetic Scene Graph Dataset With Human Object Interactions
Mrunmai Vivek Phatak, Julian Lorenz, Nico Hörmann +2
When humans and robotic agents coexist in an environment, scene understanding becomes crucial for the agents to carry out various downstream tasks like navigation and planning. Hen…
Towards Ball Spin and Trajectory Analysis in Table Tennis Broadcast Videos via Physically Grounded Synthetic-to-Real Transfer
Daniel Kienzle, Robin Schön, Rainer Lienhart +1
Analyzing a player's technique in table tennis requires knowledge of the ball's 3D trajectory and spin. While, the spin is not directly observable in standard broadcasting videos,…
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…
On the Importance of Conditioning for Privacy-Preserving Data Augmentation
Julian Lorenz, Katja Ludwig, Valentin Haug +1
Latent diffusion models can be used as a powerful augmentation method to artificially extend datasets for enhanced training. To the human eye, these augmented images look very diff…
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…
Leveraging Anthropometric Measurements to Improve Human Mesh Estimation and Ensure Consistent Body Shapes
Katja Ludwig, Julian Lorenz, Daniel Kienzle +2
The basic body shape (i.e., the body shape in T-pose) of a person does not change within a single video. However, most SOTA human mesh estimation (HME) models output a slightly dif…
WSESeg: Introducing a Dataset for the Segmentation of Winter Sports Equipment with a Baseline for Interactive Segmentation
Robin Schön, Daniel Kienzle, Rainer Lienhart
In this paper we introduce a new dataset containing instance segmentation masks for ten different categories of winter sports equipment, called WSESeg (Winter Sports Equipment Segm…
A Fair Ranking and New Model for Panoptic Scene Graph Generation
Julian Lorenz, Alexander Pest, Daniel Kienzle +2
In panoptic scene graph generation (PSGG), models retrieve interactions between objects in an image which are grounded by panoptic segmentation masks. Previous evaluations on panop…
Segformer++: Efficient Token-Merging Strategies for High-Resolution Semantic Segmentation
Daniel Kienzle, Marco Kantonis, Robin Schön +1
Utilizing transformer architectures for semantic segmentation of high-resolution images is hindered by the attention's quadratic computational complexity in the number of tokens. A…