Proceedings Of The 2022 Joint Workshop Of Fraunhofer Iosb And Institute For Anthropomatics Vision And Fusion Laboratory
Download Proceedings Of The 2022 Joint Workshop Of Fraunhofer Iosb And Institute For Anthropomatics Vision And Fusion Laboratory PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Proceedings Of The 2022 Joint Workshop Of Fraunhofer Iosb And Institute For Anthropomatics Vision And Fusion Laboratory book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Proceedings of the 2022 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
Author: Beyerer, Jürgen
language: en
Publisher: KIT Scientific Publishing
Release Date: 2023-07-05
In August 2022, Fraunhofer IOSB and IES of KIT held a joint workshop in a Schwarzwaldhaus near Triberg. Doctoral students presented research reports and discussed various topics like computer vision, optical metrology, network security, usage control, and machine learning. This book compiles the workshop's results and ideas, offering a comprehensive overview of the research program of IES and Fraunhofer IOSB.
Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
Author: Beyerer, Jürgen
language: en
Publisher: KIT Scientific Publishing
Release Date: 2022-07-05
2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports.
Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving
Author: Kalb, Tobias Michael
language: en
Publisher: KIT Scientific Publishing
Release Date: 2024-10-21
Deep learning excels at extracting complex patterns but faces catastrophic forgetting when fine-tuned on new data. This book investigates how class- and domain-incremental learning affect neural networks for automated driving, identifying semantic shifts and feature changes as key factors. Tools for quantitatively measuring forgetting are selected and used to show how strategies like image augmentation, pretraining, and architectural adaptations mitigate catastrophic forgetting.