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Data augmentation is a key component of CNN based image recognition tasks like object detection.
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2019
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B. Li, W. Ouyang, L. Sheng, X. Zeng, and X. Wang, “Gs3d: An efficient 3d object detection framework for autonomous driving,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019
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Z. Liu, Z. Wu, and R. Tóth, “Smoke: single-stage monocular 3d object detection via keypoint estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2020, pp. 996–997
2020
Later among the works it cites.
J.-H. Lee, M. Z. Zaheer, M. Astrid, and S.-I. Lee, “Smoothmix: a simple yet effective data augmentation to train robust classifiers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2020, pp. 756–757
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Later among the works it cites.
2020
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2020
Later among the works it cites.
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S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, and Y. Yoo, “Cutmix: Regularization strategy to train strong classifiers with localizable features,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 6023–6032
2019
Cited alongside, same era.
2020
Cited alongside, same era.
P. Li, H. Zhao, P. Liu, and F. Cao, “Rtm3d: Real-time monocular 3d detection from object keypoints for autonomous driving,” in Computer Vision – ECCV 2020 , A. Vedaldi, H. Bischof, T. Brox, and J.-M. Frahm, Eds. Cham: Springer International Publishing, 2020, pp. 644–660
2020
Cited alongside, same era.
A. Buslaev, V. I. Iglovikov, E. Khvedchenya, A. Parinov, M. Druzhinin, and A. A. Kalinin, “Albumentations: Fast and flexible image augmentations,” Information , vol. 11, no. 2, 2020. [Online]. Available: https://www.mdpi.com/2078-2489/11/2/125
2078
Closest in time.
F. Manhardt, W. Kehl, and A. Gaidon, “Roi-10d: Monocular lifting of 2d detection to 6d pose and metric shape,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 2069–2078
2078
Closest in time.