Hi!. I am Sebastian Schmidt, a PhD student at the Technical University of Munich (TUM) under the supervision of Prof. Dr. Stephan Günnemann in cooperation with BMW Group. At BMW, I am within the planning and perception research team. Previously, I was part of the autonomous driving division of BMW.
@inproceedings{Schmidt2025b,title={Prior2Former - Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation},author={Schmidt, Sebastian and K\"orner, Julius and Fuchsgruber, Dominik and Gasperini, Stefano and Tombari, Federico and G\"unnemann, Stephan},year={2025},journal={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) - Highlight},url={http://arxiv.org/abs/2405.11337},volume={2405.11337},eprint={arXiv:2405.11337},}
Joint Out-of-Distribution Filtering and Data Discovery Active Learning
Sebastian Schmidt, Leonard Schenk, Leo Schwinn, and 1 more author
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025
As the data demand for deep learning models increases, active learning (AL) becomes essential to strategically select samples for labeling, which maximizes data efficiency and reduces training costs. Real-world scenarios necessitate the consideration of incomplete data knowledge within AL. Prior works address handling out-of-distribution (OOD) data, while another research direction has focused on category discovery. However, a combined analysis of real-world considerations combining AL with out-of-distribution data and category discovery remains unexplored. To address this gap, we propose Joint Out-of-distribution filtering and data Discovery Active learning (Joda) , to uniquely address both challenges simultaneously by filtering out OOD data before selecting candidates for labeling. In contrast to previous methods, we deeply entangle the training procedure with filter and selection to construct a common feature space that aligns known and novel categories while separating OOD samples. Unlike previous works, Joda is highly efficient and completely omits auxiliary models and training access to the unlabeled pool for filtering or selection. In extensive experiments on 18 configurations and 3 metrics, \ours{} consistently achieves the highest accuracy with the best class discovery to OOD filtering balance compared to state-of-the-art competitor approaches.
@inproceedings{Schmidt2025a,author={Schmidt, Sebastian and Schenk, Leonard and Schwinn, Leo and Günnemann, Stephan},booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},title={Joint Out-of-Distribution Filtering and Data Discovery Active Learning},url={http://arxiv.org/abs/2503.02491},year={2025},}
Generalized Synchronized Active Learning for Multi-Agent-Based Data Selection on Mobile Robotic Systems
Sebastian Schmidt, Lukas Stappen, Leo Schwinn, and 1 more author
@article{Schmidt2024b,author={Schmidt, Sebastian and Stappen, Lukas and Schwinn, Leo and Günnemann, Stephan},journal={IEEE Robotics and Automation Letters},title={Generalized Synchronized Active Learning for Multi-Agent-Based Data Selection on Mobile Robotic Systems},year={2024},volume={9},number={10},pages={8659-8666},keywords={Robots;Robot kinematics;Uncertainty;Data centers;Synchronization;Task analysis;Streams;Computer vision for transportation;deep learning for visual perception;deep learning methods},url={https://ieeexplore.ieee.org/abstract/document/10637683},doi={10.1109/LRA.2024.3444670},}
GeoDiffusion: A Training-Free Framework for Accurate 3D Geometric Conditioning in Image Generation
Phillip Mueller, Talip Uenlue, Sebastian Schmidt, and 4 more authors
In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Oct 2025
@inproceedings{mueller2025,title={GeoDiffusion: A Training-Free Framework for Accurate 3D Geometric Conditioning in Image Generation},author={Mueller, Phillip and Uenlue, Talip and Schmidt, Sebastian and Kollovieh, Marcel and Fan, Jiajie and G\"unnemann, Stephan and Mikelsons, Lars},year={2025},month=oct,booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},pages={6374--6384},url={TBD},}
