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• W3010347989 abstract "Mixup - a neural network regularization technique based on linear interpolation of labeled sample pairs - has stood out by its capacity to improve model's robustness and generalizability through a surprisingly simple formalism. However, its extension to the field of object detection remains unclear as the interpolation of bounding boxes cannot be naively defined. In this paper, we propose to leverage the inherent region mapping structure of anchors to introduce a mixup-driven training regularization for region proposal based object detectors. The proposed method is benchmarked on standard datasets with challenging detection settings. Our experiments show an enhanced robustness to image alterations along with an ability to decontextualize detections, resulting in an improved generalization power." @default.
• W3010347989 created "2020-03-13" @default.
• W3010347989 creator A5025428539 @default.
• W3010347989 creator A5065243427 @default.
• W3010347989 date "2020-03-04" @default.
• W3010347989 modified "2023-09-25" @default.
• W3010347989 title "Mixup Regularization for Region Proposal based Object Detectors" @default.
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• W3010347989 hasPublicationYear "2020" @default.
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