Julian Lorenz
Lehre
- SoSe 2022: Multimedia Projekt
- WiSe 2022/2023: Multimedia Projekt
- SoSe 2023: Multimedia Projekt
Veröffentlichungen
2023 |
Katja Ludwig, Julian Lorenz, Robin Schön and Rainer Lienhart. in press. All keypoints you need: detecting arbitrary keypoints on the body of triple, high, and long jump athletes. In 2023 IEEE/CVF International Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, Piscataway, NJ |
Daniel Kienzle, Julian Lorenz, Robin Schön, Katja Ludwig and Rainer Lienhart. 2023. COVID detection and severity prediction with 3D-ConvNeXt and custom pretrainings. In Leonid Karlinsky, Tomer Michaeli, Ko Nishino (Eds.). Computer Vision – ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VII. Springer, Berlin, 500-516 DOI: 10.1007/978-3-031-25082-8_33 |
Katja Ludwig, Daniel Kienzle, Julian Lorenz and Rainer Lienhart. in press. Detecting arbitrary keypoints on limbs and skis with sparse partly correct segmentation masks. In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (Workshops). IEEE, Piscataway, NJ |
Julian Lorenz, Florian Barthel, Daniel Kienzle and Rainer Lienhart. in press. Haystack: a panoptic scene graph dataset to evaluate rare predicate classes. In IEEE International Conference on Computer Vision Workshops (ICCV 2023 Workshops), October 2-6, 2023, Paris, France. IEEE, New York, NY |
Luuk H. Boulogne, Julian Lorenz, Daniel Kienzle, Robin Schön, Katja Ludwig, Rainer Lienhart, Simon Jegou, Guang Li, Cong Chen, Qi Wang, Derik Shi, Mayug Maniparambil, Dominik Müller, Silvan Mertes, Niklas Schröter, Fabio Hellmann, Miriam Elia, Ine Dirks, Matias Nicolas Bossa, Abel Diaz Berenguer, Tanmoy Mukherjee, Jef Vandemeulebroucke, Hichem Sahli, Nikos Deligiannis, Panagiotis Gonidakis, Ngoc Dung Huynh, Imran Razzak, Reda Bouadjenek, Mario Verdicchio, Pasquale Borrelli, Marco Aiello, James A. Meakin, Alexander Lemm, Christoph Russ, Razvan Ionasec, Nikos Paragios, Bram van Ginneken and Marie-Pierre Revel Dubios. in press. The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data. preprint. |