62 citations · 153 across the 21 of their papers we have counts for
21 papers
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.…
Impact of Pseudo Depth on Open World Object Segmentation with Minimal User Guidance
Robin Schön, Katja Ludwig, Rainer Lienhart
Pseudo depth maps are depth map predicitions which are used as ground truth during training. In this paper we leverage pseudo depth maps in order to segment objects of classes that…
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…
Uplift and Upsample: Efficient 3D Human Pose Estimation with Uplifting Transformers
Moritz Einfalt, Katja Ludwig, Rainer Lienhart
The state-of-the-art for monocular 3D human pose estimation in videos is dominated by the paradigm of 2D-to-3D pose uplifting. While the uplifting methods themselves are rather eff…
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…