Intel/VALERIE22

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The declared licence (cc-by-4.0) is classified as permissive.

Declared licence: cc-by-4.0, from the tagEvidence: README.md at 040fedf
Licence files: No licence file at the top level of the repositoryEvidence: file list at 040fedf
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Citation text from the card

@misc{grau2023valerie22,
    title={VALERIE22 -- A photorealistic, richly metadata annotated dataset of urban environments},
    author={Oliver Grau and Korbinian Hagn},
    year={2023},
    eprint={2308.09632},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@inproceedings{hagn2022increasing,
  title={Increasing pedestrian detection performance through weighting of detection impairing factors},
  author={Hagn, Korbinian and Grau, Oliver},
  booktitle={Proceedings of the 6th ACM Computer Science in Cars Symposium},
  pages={1--10},
  year={2022}
}

@inproceedings{hagn2022validation,
  title={Validation of Pedestrian Detectors by Classification of Visual Detection Impairing Factors},
  author={Hagn, Korbinian and Grau, Oliver},
  booktitle={European Conference on Computer Vision},
  pages={476--491},
  year={2022},
  organization={Springer}
}

@incollection{grau2022variational,
  title={A variational deep synthesis approach for perception validation},
  author={Grau, Oliver and Hagn, Korbinian and Syed Sha, Qutub},
  booktitle={Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty Quantification, and Insights Towards Safety},
  pages={359--381},
  year={2022},
  publisher={Springer International Publishing Cham}
}

@incollection{hagn2022optimized,
  title={Optimized data synthesis for DNN training and validation by sensor artifact simulation},
  author={Hagn, Korbinian and Grau, Oliver},
  booktitle={Deep Neural Networks and Data for Automated Dri

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dataset_licence({ "id": "Intel/VALERIE22" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0