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09/08/2023
Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes
In the life cycle of highly automated systems operating in an open and d...
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08/16/2023
ResBuilder: Automated Learning of Depth with Residual Structures
In this work, we develop a neural architecture search algorithm, termed ...
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05/23/2023
survAIval: Survival Analysis with the Eyes of AI
In this study, we propose a novel approach to enrich the training data f...
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04/30/2023
Detecting Novelties with Empty Classes
For open world applications, deep neural networks (DNNs) need to be awar...
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04/24/2023
Augmentation-based Domain Generalization for Semantic Segmentation
Unsupervised Domain Adaptation (UDA) and domain generalization (DG) are ...
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04/24/2023
Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving
Deep neural networks (DNNs) have proven their capabilities in many areas...
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04/14/2023
Who breaks early, looses: goal oriented training of deep neural networks based on port Hamiltonian dynamics
The highly structured energy landscape of the loss as a function of para...
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02/28/2023
Non-convex shape optimization by dissipative Hamiltonian flows
Shape optimization with constraints given by partial differential equati...
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02/21/2023
LU-Net: Invertible Neural Networks Based on Matrix Factorization
LU-Net is a simple and fast architecture for invertible neural networks ...
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02/20/2023
Generalization capabilities of conditional GAN for turbulent flow under changes of geometry
Turbulent flow consists of structures with a wide range of spatial and t...
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02/03/2023
Ridepooling and public bus services: A comparative case-study
This case-study aims at a comparison of the service quality of time-tabl...
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01/08/2023
Equivariant and Steerable Neural Networks: A review with special emphasis on the symmetric group
Convolutional neural networks revolutionized computer vision and natrual...
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12/21/2022
Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection
Active learning as a paradigm in deep learning is especially important i...
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10/05/2022
Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects
In this work we present two video test data sets for the novel computer ...
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08/18/2022
Semi-supervised domain adaptation with CycleGAN guided by a downstream task loss
Domain adaptation is of huge interest as labeling is an expensive and er...
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06/09/2022
What should AI see? Using the Public's Opinion to Determine the Perception of an AI
Deep neural networks (DNN) have made impressive progress in the interpre...
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05/30/2022
Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning
Bringing deep neural networks (DNNs) into safety critical applications s...
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02/22/2022
A-Eye: Driving with the Eyes of AI for Corner Case Generation
The overall goal of this work is to enrich training data for automated d...
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02/17/2022
Detecting and Learning the Unknown in Semantic Segmentation
Semantic segmentation is a crucial component for perception in automated...
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01/04/2022
Towards Unsupervised Open World Semantic Segmentation
For the semantic segmentation of images, state-of-the-art deep neural ne...
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12/09/2021
Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance?
While automated driving is often advertised with better-than-human drivi...
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12/05/2021
Generative Modeling of Turbulence
We present a mathematically well founded approach for the synthetic mode...
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10/29/2021
False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation
We present a novel post-processing tool for semantic segmentation of LiD...
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09/20/2021
Background-Foreground Segmentation for Interior Sensing in Automotive Industry
To ensure safety in automated driving, the correct perception of the sit...
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07/09/2021
Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors
Reliable epistemic uncertainty estimation is an essential component for ...
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12/14/2020
Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates
Instance segmentation with neural networks is an essential task in envir...
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12/09/2020
Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentation
Deep neural networks (DNNs) for the semantic segmentation of images are ...
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11/24/2020
A Convenient Infinite Dimensional Framework for Generative Adversarial Learning
In recent years, generative adversarial networks (GANs) have demonstrate...
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10/07/2020
YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors
In this work, we present an uncertainty-based method for sensor fusion w...
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10/05/2020
MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps
We present a novel region based active learning method for semantic imag...
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09/23/2020
Detection of Iterative Adversarial Attacks via Counter Attack
Deep neural networks (DNNs) have proven to be powerful tools for process...
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04/23/2020
Coarsening in Algebraic Multigrid using Gaussian Processes
Multigrid methods have proven to be an invaluable tool to efficiently so...
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04/22/2020
Tracing locally Pareto optimal points by numerical integration
We suggest a novel approach for the efficient and reliable approximation...
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02/20/2020
GivEn – Shape Optimization for Gas Turbines in Volatile Energy Networks
This paper describes the project GivEn that develops a novel multicriter...
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12/16/2019
MetaFusion: Controlled False-Negative Reduction of Minority Classes in Semantic Segmentation
In semantic segmentation datasets, classes of high importance are oftent...
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12/08/2019
Detection of False Positive and False Negative Samples in Semantic Segmentation
In recent years, deep learning methods have outperformed other methods i...
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11/12/2019
Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks
In the semantic segmentation of street scenes, the reliability of a pred...
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07/02/2019
The Ethical Dilemma when (not) Setting up Cost-based Decision Rules in Semantic Segmentation
Neural networks for semantic segmentation can be seen as statistical mod...
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01/25/2019
Using adjoint CFD to quantify the impact of manufacturing variations on a heavy duty turbine vane
We consider the evaluation of manufacturing variations to the aerodynami...
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01/24/2019
Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation
As part of autonomous car driving systems, semantic segmentation is an e...
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11/01/2018
Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities
We present a method that "meta" classifies whether segments (objects) pr...
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05/22/2018
Classification Uncertainty of Deep Neural Networks Based on Gradient Information
We study the quantification of uncertainty of Convolutional Neural Netwo...
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03/03/2018
