Ambiguous Annotations: When is a Pedestrian not a Pedestrian?

arXiv:2405.08794v1 Announce Type: new
Abstract: Datasets labelled by human annotators are widely used in the training and testing of machine learning models. In recent years, researchers are increasingly paying attention to label quality. However, it is not always possible to objectively determine whether an assigned label is correct or not. The present work investigates this ambiguity in the annotation of autonomous driving datasets as an important dimension of data quality. Our experiments show that excluding highly ambiguous data from the …

abstract annotations arxiv attention cs.cv datasets however human machine machine learning machine learning models pedestrian quality researchers testing training type