Riedlinger, Tobias: Methods and applications of uncertainty quantification for object recognition. Wuppertal, 2023
Content
Acknowledgements
Foreword
Introduction
Foundations
Statistical Learning Theory
Deep Learning
Architectures
Training Neural Networks
Universal Approximation
Single-Layer Perceptrons
Perceptrons with ReLU Activation
Convolutional Neural Networks
Transformer Neural Networks
Uncertainty Quantification
Advanced Computer Vision Tasks
Gradient Uncertainty for Deep Object Detectors
Introduction
Related Work
Method
Experiments
Implementation Details
Comparison with Output-based Uncertainty
Generalization over Architectures
Calibration
Pedestrian Detection
MetaFusion
Runtime
Conclusion
Gradient Uncertainty in Semantic Segmentation
Introduction
Related Work
Methods
Experiments
Implementation Details
Pixel-wise Uncertainty Quantification
Segment-wise Uncertainty Quantification
Out-of-Distribution Segmentation
Runtime
Conclusion
Rapid Prototyping of Active Learning for Object Detection
Introduction
Related Work
Methods
Experiments
Implementation Details
Benchmark Results
Generalization of Sandbox Results
Image-Aggregation Methods
Runtime
Conclusion
Label Error Identification For Object Detection Datasets
Prediction Quality Estimation for Lidar Object Detection
Introduction
Related Work
Methods
Experiments
Implementation Details
Correlation of Features and IoUBEV
Meta Classification Models
Generalization over Datasets and Networks
Feature Selection
Confidence Calibration
Label Error Detection
Conclusion
Conclusion and Outlook
List of Notations