1 citations · 1 across the 4 of their papers we have counts for
5 papers
Object Detection Benchmarks are Incomplete: The Role of Label Errors and Annotation Uncertainty
Sarina Penquitt, Jonathan Klees, Antonia van Betteray +4
While object detection has advanced through improved architectures and open-vocabulary models, we provide strong evidence that benchmark quality is limited by annotation incomplete…
Learning to Detect Label Errors by Making Them: A Method for Segmentation and Object Detection Datasets
Sarina Penquitt, Tobias Riedlinger, Timo Heller +2
Recently, detection of label errors and improvement of label quality in datasets for supervised learning tasks has become an increasingly important goal in both research and indust…
From Label Error Detection to Correction: A Modular Framework and Benchmark for Object Detection Datasets
Sarina Penquitt, Jonathan Klees, Rinor Cakaj +3
Object detection has advanced rapidly in recent years, driven by increasingly large and diverse datasets. However, label errors often compromise the quality of these datasets and a…
LMD: Light-weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds
Tobias Riedlinger, Marius Schubert, Sarina Penquitt +7
Object detection on Lidar point cloud data is a promising technology for autonomous driving and robotics which has seen a significant rise in performance and accuracy during recent…
LU-Net: Invertible Neural Networks Based on Matrix Factorization
Robin Chan, Sarina Penquitt, Hanno Gottschalk
LU-Net is a simple and fast architecture for invertible neural networks (INN) that is based on the factorization of quadratic weight matrices , where is…