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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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06/13/2023
LMD: Light-weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds
Object detection on Lidar point cloud data is a promising technology for...
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03/13/2023
Identifying Label Errors in Object Detection Datasets by Loss Inspection
Labeling datasets for supervised object detection is a dull and time-con...
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12/29/2022
AttEntropy: Segmenting Unknown Objects in Complex Scenes using the Spatial Attention Entropy of Semantic Segmentation Transformers
Vision transformers have emerged as powerful tools for many computer vis...
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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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11/10/2022
MGiaD: Multigrid in all dimensions. Efficiency and robustness by coarsening in resolution and channel dimensions
Current state-of-the-art deep neural networks for image classification a...
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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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07/13/2022
Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification
In this work, we for the first time present a method for detecting label...
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07/07/2022
False Negative Reduction in Semantic Segmentation under Domain Shift using Depth Estimation
State-of-the-art deep neural networks demonstrate outstanding performanc...
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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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05/18/2022
Coarsest-level improvements in multigrid for lattice QCD on large-scale computers
Numerical simulations of quantum chromodynamics (QCD) on a lattice requi...
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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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01/31/2022
UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs
We present an approach to quantifying both aleatoric and epistemic uncer...
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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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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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06/10/2021
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis
Many machine learning applications can benefit from simulated data for s...
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04/30/2021
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation
State-of-the-art semantic or instance segmentation deep neural networks ...
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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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10/04/2020
MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection
In object detection with deep neural networks, the box-wise objectness s...
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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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05/14/2020
Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentation
When deploying deep learning technology in self-driving cars, deep neura...
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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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04/09/2019
Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images
In the semantic segmentation of street scenes the reliability of the pre...
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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
