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DNN Analysis through Synthetic Data Variation.

, , и . CSCS, стр. 4:1-4:10. ACM, (2020)

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Improved Sensor Model for Realistic Synthetic Data Generation., и . CSCS, стр. 4:1-4:9. ACM, (2021)SynPeDS: A Synthetic Dataset for Pedestrian Detection in Urban Traffic Scenes., , , , , , , , , и . CSCS, стр. 2:1-2:10. ACM, (2022)DNN Analysis through Synthetic Data Variation., , и . CSCS, стр. 4:1-4:10. ACM, (2020)Increasing pedestrian detection performance through weighting of detection impairing factors., и . CSCS, стр. 1:1-1:10. ACM, (2022)VALERIE22 - A photorealistic, richly metadata annotated dataset of urban environments., и . CSCS, стр. 6:1-6:9. ACM, (2023)BEA: Revisiting anchor-based object detection DNN using Budding Ensemble Architecture., , , , , , , , и . CoRR, (2023)Towards machine learning-based optimal has, , , , и . arXiv preprint arXiv:1808.08065, (2018)Keep calm and don’t switch: about the relationship between switches and quality in HAS, , , , и . 2017 29th International Teletraffic Congress (ITC 29), 3, стр. 1--6. IEEE, (2017)Validation of Pedestrian Detectors by Classification of Visual Detection Impairing Factors., и . ECCV Workshops (5), том 13805 из Lecture Notes in Computer Science, стр. 476-491. Springer, (2022)BEA: Revisiting anchor-based object detection DNN using Budding Ensemble Architecture., , , , , , , , и . BMVC, стр. 792-797. BMVA Press, (2023)