Improving 3D Object Detection Through Progressive Population Based Augmentation-Reference-Cited by-同舟云学术

Improving 3D Object Detection Through Progressive Population Based Augmentation

Published:2020 Issue: Volume: Page:279-294
ISSN:0302-9743
Container-title:Computer Vision – ECCV 2020
language:
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Author:

Cheng Shuyang,Leng Zhaoqi,Cubuk Ekin Dogus,Zoph Barret,Bai Chunyan,Ngiam Jiquan,Song Yang,Caine Benjamin,Vasudevan Vijay,Li Congcong,Le Quoc V.,Shlens Jonathon,Anguelov Dragomir

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-58589-1_17

Reference38 articles.

1. Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3D object detection network for autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1907–1915 (2017)

2. Cho, H., Seo, Y.W., Kumar, B.V., Rajkumar, R.R.: A multi-sensor fusion system for moving object detection and tracking in urban driving environments. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 1836–1843. IEEE (2014)

3. Ciregan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642–3649. IEEE (2012)

4. Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: Autoaugment: learning augmentation policies from data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)

5. Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: Randaugment: practical data augmentation with no separate search. arXiv preprint arXiv:1909.13719 (2019)

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