2 citations · 6 across the 18 of their papers we have counts for
19 papers
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…
DSFlash: Comprehensive Panoptic Scene Graph Generation in Realtime
Julian Lorenz, Vladyslav Kovganko, Elias Kohout +3
Scene Graph Generation (SGG) aims to extract a detailed graph structure from an image, a representation that holds significant promise as a robust intermediate step for complex dow…
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…
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…
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…
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…
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…
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…
A Review and Efficient Implementation of Scene Graph Generation Metrics
Julian Lorenz, Robin Schön, Katja Ludwig +1
Scene graph generation has emerged as a prominent research field in computer vision, witnessing significant advancements in the recent years. However, despite these strides, precis…
Adapting the Segment Anything Model During Usage in Novel Situations
Robin Schön, Julian Lorenz, Katja Ludwig +1
The interactive segmentation task consists in the creation of object segmentation masks based on user interactions. The most common way to guide a model towards producing a correct…
Towards Learning Monocular 3D Object Localization From 2D Labels using the Physical Laws of Motion
Daniel Kienzle, Julian Lorenz, Katja Ludwig +1
We present a novel method for precise 3D object localization in single images from a single calibrated camera using only 2D labels. No expensive 3D labels are needed. Thus, instead…
Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate Classes
Julian Lorenz, Florian Barthel, Daniel Kienzle +1
Current scene graph datasets suffer from strong long-tail distributions of their predicate classes. Due to a very low number of some predicate classes in the test sets, no reliable…
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
Luuk H. Boulogne, Julian Lorenz, Daniel Kienzle +35
Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Publ…
All Keypoints You Need: Detecting Arbitrary Keypoints on the Body of Triple, High, and Long Jump Athletes
Katja Ludwig, Julian Lorenz, Robin Schön +1
Performance analyses based on videos are commonly used by coaches of athletes in various sports disciplines. In individual sports, these analyses mainly comprise the body posture.…
Detecting Arbitrary Keypoints on Limbs and Skis with Sparse Partly Correct Segmentation Masks
Katja Ludwig, Daniel Kienzle, Julian Lorenz +1
Analyses based on the body posture are crucial for top-class athletes in many sports disciplines. If at all, coaches label only the most important keypoints, since manual annotatio…
COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings
Daniel Kienzle, Julian Lorenz, Robin Schön +2
Since COVID strongly affects the respiratory system, lung CT-scans can be used for the analysis of a patients health. We introduce a neural network for the prediction of the severi…