{
 "id": "Intel/VALERIE22",
 "owner": "Intel",
 "name": "VALERIE22",
 "downloads": 205,
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 "last_modified": "2024-01-29T09:02:19.000Z",
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 "tasks": [
  "image-segmentation",
  "object-detection"
 ],
 "citation": "@misc{grau2023valerie22,\n    title={VALERIE22 -- A photorealistic, richly metadata annotated dataset of urban environments},\n    author={Oliver Grau and Korbinian Hagn},\n    year={2023},\n    eprint={2308.09632},\n    archivePrefix={arXiv},\n    primaryClass={cs.CV}\n}\n\n@inproceedings{hagn2022increasing,\n  title={Increasing pedestrian detection performance through weighting of detection impairing factors},\n  author={Hagn, Korbinian and Grau, Oliver},\n  booktitle={Proceedings of the 6th ACM Computer Science in Cars Symposium},\n  pages={1--10},\n  year={2022}\n}\n\n@inproceedings{hagn2022validation,\n  title={Validation of Pedestrian Detectors by Classification of Visual Detection Impairing Factors},\n  author={Hagn, Korbinian and Grau, Oliver},\n  booktitle={European Conference on Computer Vision},\n  pages={476--491},\n  year={2022},\n  organization={Springer}\n}\n\n@incollection{grau2022variational,\n  title={A variational deep synthesis approach for perception validation},\n  author={Grau, Oliver and Hagn, Korbinian and Syed Sha, Qutub},\n  booktitle={Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty Quantification, and Insights Towards Safety},\n  pages={359--381},\n  year={2022},\n  publisher={Springer International Publishing Cham}\n}\n\n@incollection{hagn2022optimized,\n  title={Optimized data synthesis for DNN training and validation by sensor artifact simulation},\n  author={Hagn, Korbinian and Grau, Oliver},\n  booktitle={Deep Neural Networks and Data for Automated Dri",
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