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Prediction rating and performance improvement for segmentation networks by time-dynamic uncertainty estimates / eingereicht von Kira Maag, M. Sc. Wuppertal, 03.08.2021
Content
Acknowledgments
Foreword
Contents
Introduction
Review of Basic Material and Related Work
Neural Networks
Feed Forward Neural Networks
Convolutional Neural Networks
Semantic Segmentation
Instance Segmentation
Depth Estimation
Evaluation Metrics
Uncertainty Quantification
Object Tracking
Related Work
Evaluation Methods
Classification and Regression Methods for the Prediction of the IoU
Linear Regression
Gradient Boosting
Shallow Neural Networks
Performance Measures
Datasets
Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks
Related Work
Method
Tracking Segments over Time
Segment-wise Metrics and Time Series
Prediction of the IoU from Time Series
Numerical Results
VIPER Dataset
KITTI Dataset
Discussion
Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates
Related Work
Method
Tracking Method for Instances
Temporal Instance-wise Metrics
IoU Prediction
Numerical Results
Evaluation of our Tracking Algorithm
Meta Classification and Regression
Advanced Score Values
Discussion
False Negative Reduction in Video Instance Segmentation using Uncertainty Estimates
Related Work
Method
Detection Algorithm
Metrics
Meta Classification
Numerical Results
Meta Classification
Evaluation of the Detection Method
Discussion
Conclusions & Outlook
List of Figures
List of Tables
List of Algorithms
List of Notations
Bibliography