1 citations · 2 across the 5 of their papers we have counts for
5 papers
Semi-Supervised Diversity-Aware Domain Adaptation for 3D Object detection
Bartłomiej Olber, Jakub Winter, Paweł Wawrzyński +5
3D object detectors are fundamental components of perception systems in autonomous vehicles. While these detectors achieve remarkable performance on standard autonomous driving ben…
Semi-Automated Data Annotation in Multisensor Datasets for Autonomous Vehicle Testing
Andrii Gamalii, Daniel Górniak, Robert Nowak +3
This report presents the design and implementation of a semi-automated data annotation pipeline developed within the DARTS project, whose goal is to create a large-scale, multimoda…
Detecting Out-of-distribution Objects Using Neuron Activation Patterns
Bartłomiej Olber, Krystian Radlak, Krystian Chachuła +2
Object detection is essential to many perception algorithms used in modern robotics applications. Unfortunately, the existing models share a tendency to assign high confidence scor…
Detection of out-of-distribution samples using binary neuron activation patterns
Bartlomiej Olber, Krystian Radlak, Adam Popowicz +2
Deep neural networks (DNN) have outstanding performance in various applications. Despite numerous efforts of the research community, out-of-distribution (OOD) samples remain a sign…
Combating noisy labels in object detection datasets
Krystian Chachuła, Jakub Łyskawa, Bartłomiej Olber +3
The quality of training datasets for deep neural networks is a key factor contributing to the accuracy of resulting models. This effect is amplified in difficult tasks such as obje…