Get our free extension to see links to code for papers anywhere online!

Add to Chrome

Add to Firefox

Picture for Marius Schubert

Marius Schubert

Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection

Dec 21, 2022
Tobias Riedlinger, Marius Schubert, Karsten Kahl, Hanno Gottschalk, Matthias Rottmann

Figure 1 for Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection

Figure 2 for Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection

Figure 3 for Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection

Figure 4 for Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection

* 17 pages, 12 figures, 9 tables

Access Paper or Ask Questions

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

Jul 09, 2021
Tobias Riedlinger, Matthias Rottmann, Marius Schubert, Hanno Gottschalk

Figure 1 for Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

Figure 2 for Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

Figure 3 for Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

Figure 4 for Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

* 20 pages, 8 figures, 7 tables

Access Paper or Ask Questions

MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection

Oct 06, 2020
Marius Schubert, Karsten Kahl, Matthias Rottmann

Figure 1 for MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection

Figure 2 for MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection

Figure 3 for MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection

Figure 4 for MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection

* 11 pages, 5 figures, 5 tables

Access Paper or Ask Questions

Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images

Apr 09, 2019
Matthias Rottmann, Marius Schubert

Figure 1 for Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images

Figure 2 for Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images

Figure 3 for Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images

Figure 4 for Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images

Access Paper or Ask Questions