Publications

2025

1. Prior2Former - Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation

   Sebastian Schmidt, Julius Körner, Dominik Fuchsgruber, and 3 more authors

   arXiv, 2025
2. 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

   Abs

   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.

2024

1. A Unified Approach Towards Active Learning and Out-of-Distribution Detection

   Sebastian Schmidt, Leonard Schenk, Leonard Schwinn, and 1 more author

   arXiv, 2024
2. Generalized Synchronized Active Learning for Multi-Agent-Based Data Selection on Mobile Robotic Systems

   Sebastian Schmidt, Lukas Stappen, Leo Schwinn, and 1 more author

   IEEE Robotics and Automation Letters, 2024

   DOI PDF
3. Deep Sensor Fusion with Constraint Safety Bounds for High Precision Localization

   Sebastian Schmidt, Ludwig Stumpp, Diego Valverde, and 1 more author

   In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024

   PDF

2023

1. Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss

   Sebastian Schmidt and Stephan Günnemann

   In Proceedings of the British Machine Vision Conference (BMVC), 2023

   PDF

2020

1. Advanced Active Learning Strategies for Object Detection

   Sebastian Schmidt, Qing Rao, Julian Tatsch, and 1 more author

   In Proceedings of the IEEE Intelligent Vehicles Symposium (IV), 2020

   Abs PDF

   Future self-driving cars must be able to perceive and understand their surroundings. Deep learning based approaches promise to solve the perception problem but require a large amount of manually labeled training data. Active learning is a training procedure in which the model itself selects interesting samples for labeling based on their uncertainty, with substantially less data required for training. Recent research in active learning has mostly focused on the simple image classification task. In this paper, we propose novel methods to estimate sample uncertainties for 2D and 3D object detection using Ensembles. We moreover evaluate different training strategies including Continuous Training to alleviate increasing training times introduced by the active learning cycle. Finally, we investigate the effects of active learning on imbalanced datasets and possible interactions with class weighting. Experiment results show both increased time saving around 55% and data saving rates of around 30%. For the 3D object detection task, we show that our proposed uncertainty estimation method is valid, saving 35% of labeling efforts and thus is ready for application for automotive object detection use cases.