Learning an Uncertainty-Aware Object Detector for Autonomous Driving

D. J. C. MacKay, “A practical Bayesian framework for backpropagation networks,” Neural Computation , vol. 4, no. 3, pp. 448–472, 1992

1992

Earlier work this paper cites.

A. Graves, “Practical variational inference for neural networks,” in Proceedings of Advances in Neural Information Processing Systems (NIPS) , 2011

2011

Earlier work this paper cites.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research , vol. 15, no. 1, pp. 1929–1958, 2014

2014

Earlier work this paper cites.

C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra, “Weight uncertainty in neural networks,” in Proceedings of the International Conference on Machine Learning (ICML) , 2015

2015

Earlier work this paper cites.

B. Li, T. Zhang, and T. Xia, “Vehicle detection from 3D lidar using fully convolutional network,” in Proceedings of Robotics: Science and Systems (RSS) , 2016

2016

Earlier work this paper cites.

Y. Gal, “Uncertainty in deep learning,” Ph.D. dissertation, University of Cambridge, 2016

2016

Earlier work this paper cites.

Y. Gal and Z. Ghahramani, “Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,” in Proceedings of the International Conference on Machine Learning (ICML) , 2016

2016

Earlier work this paper cites.

——, “Bayesian convolutional neural networks with Bernoulli approximate variational inference,” in Proceedings of the International Conference on Learning Representations (ICLR) Workshops , 2016

2016

Earlier work this paper cites.

X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3D object detection network for autonomous driving,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017

2017

Earlier work this paper cites.

A. Kendall and Y. Gal, “What uncertainties do we need in Bayesian deep learning for computer vision?” in Proceedings of Advances in Neural Information Processing Systems (NIPS) , 2017

2017

Cited alongside, same era.

Y. Zhou and O. Tuzel, “VoxelNet: End-to-end learning for point cloud based 3D object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018

2018

Cited alongside, same era.

J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. L. Waslander, “Joint 3D proposal generation and object detection from view aggregation,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018

2018

Cited alongside, same era.

C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, “Frustum pointnets for 3D object detection from RGB-D data,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018

2018

Cited alongside, same era.

D. Feng, L. Rosenbaum, F. Timm, and K. Dietmayer, “Leveraging heteroscedastic aleatoric uncertainties for robust real-time LiDAR 3D object detection,” arXiv preprint arXiv:1809.05590 , 2018

Original

2018

Later among the works it cites.

A. Malinin and M. Gales, “Predictive uncertainty estimation via prior networks,” in Proceedings of Advances in Neural Information Processing Systems (NIPS) , 2018

2018

Later among the works it cites.

S. Choi, K. Lee, S. Lim, and S. Oh, “Uncertainty-aware learning from demonstration using mixture density networks with sampling-free variance modeling,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) , 2018

2018

Later among the works it cites.

B. Jiang, R. Luo, J. Mao, T. Xiao, and Y. Jiang, “Acquisition of localization confidence for accurate object detection,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018

2018

Later among the works it cites.