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pro vyhledávání: '"Nozarian, Farzad"'
Autor:
Nozarian, Farzad, Agarwal, Shashank, Rezaeianaran, Farzaneh, Shahzad, Danish, Poibrenski, Atanas, Müller, Christian, Slusallek, Philipp
Semi-supervised 3D object detection can benefit from the promising pseudo-labeling technique when labeled data is limited. However, recent approaches have overlooked the impact of noisy pseudo-labels during training, despite efforts to enhance pseudo
Externí odkaz:
http://arxiv.org/abs/2404.17910
In safety-critical domains like automated driving (AD), errors by the object detector may endanger pedestrians and other vulnerable road users (VRU). As common evaluation metrics are not an adequate safety indicator, recent works employ approaches to
Externí odkaz:
http://arxiv.org/abs/2402.02986
Akademický článek
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This book constitutes the thoroughly refereed conference proceedings of the First International Workshop on the Foundation of Trustworthy AI - Integrating Learning, Optimization and Reasoning, TAILOR 2020, held virtually in September 2020, associated