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• W4385800690 abstract "Image anonymization is widely adapted in practice to comply with privacy regulations in many regions. However, anonymization often degrades the quality of the data, reducing its utility for computer vision development. In this paper, we investigate the impact of image anonymization for training computer vision models on key computer vision tasks (detection, instance segmentation, and pose estimation). Specifically, we benchmark the recognition drop on common detection datasets, where we evaluate both traditional and realistic anonymization for faces and full bodies. Our comprehensive experiments reflect that traditional image anonymization substantially impacts final model performance, particularly when anonymizing the full body. Furthermore, we find that realistic anonymization can mitigate this decrease in performance, where our experiments reflect a minimal performance drop for face anonymization. Our study demonstrates that realistic anonymization can enable privacy-preserving computer vision development with minimal performance degradation across a range of important computer vision benchmarks." @default.
• W4385800690 created "2023-08-15" @default.
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• W4385800690 date "2023-06-01" @default.
• W4385800690 modified "2023-10-16" @default.
• W4385800690 title "Does Image Anonymization Impact Computer Vision Training?" @default.
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• W4385800690 doi "https://doi.org/10.1109/cvprw59228.2023.00019" @default.
• W4385800690 hasPublicationYear "2023" @default.
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