J. D. Kang and J. L. Schafer. Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. Statistical Science, 22:523-539, 2007.
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom. Pointpillars: Fast encoders for object detection from point clouds. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12689-12697, 2019.
D.-H. Lee. Pseudo-label : The simple and efficient semi-supervised learning method for deep neural networks. ICML 2013 Workshop : Challenges in Representation Learning (WREPL), 07 2013.
J. Li, Z. Liu, J. Hou, and D. Liang. Dds3d: Dense pseudo-labels with dynamic threshold for semi-supervised 3d object detection. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2023.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár. Focal loss for dense object detection. In ICCV, pages 2980-2988, 2017.
C. Liu, C. Gao, F. Liu, P. Li, D. Meng, and X. Gao. Hierarchical supervision and shuffle data augmentation for 3d semi-supervised object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
Y.-C. Liu, C.-Y. Ma, Z. He, C.-W. Kuo, K. Chen, P. Zhang, B. Wu, Z. Kira, and P. Vajda. Unbiased teacher for semi-supervised object detection. In Proceedings of the International Conference on Learning Representations (ICLR), 2021a.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, 2021b.
I. Loshchilov and F. Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017.
A. W. Marshall and I. Olkin. Multivariate chebyshev inequalities. The Annals of Mathematical Statistics, pages 1001-1014, 1960.
S. J. Pan and Q. Yang. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10):1345-1359, 2010.
J. Park, C. Xu, Y. Zhou, M. Tomizuka, and W. Zhan. Detmatch: Two teachers are better than one for joint 2D and 3D semi-supervised object detection. Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, 2022.
C. R. Qi, Y. Zhou, M. Najibi, P. Sun, K. Vo, B. Deng, and D. Anguelov. Offboard 3D object detection from point cloud sequences. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021.
P. R. Rosenbaum and D. B. Rubin. The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1):41-55, 1983.
D. B. Rubin. Inference and missing data. Biometrika, 63(3):581-592, 1976.
D. B. Rubin. Using multivariate matched sampling and regression adjustment to control bias in observational studies. Journal of the American Statistical Association, 74(366a):318-328, 1979.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115:211-252, 2015.
D. O. Scharfstein, A. Rotnitzky, and J. M. Robins. Adjusting for nonignorable drop-out using semiparametric nonresponse models. Journal of the American Statistical Association, 94(448): 1096-1120, 1999.
V. Semenova, M. Goldman, V. Chernozhukov, and M. Taddy. Estimation and inference on heterogeneous treatment effects in high-dimensional dynamic panels. arXiv preprint arXiv:1712.09988, 2017.