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Jiquan Ngiam | Zhifeng Chen | Jonathon Shlens | Benjamin Caine | Yuning Chai | Rebecca Roelofs | Vijay Vasudevan | Jonathon Shlens | Z. Chen | Vijay Vasudevan | Jiquan Ngiam | R. Roelofs | Yuning Chai | Benjamin Caine
[1] Kaiming He,et al. Data Distillation: Towards Omni-Supervised Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[2] Jiquan Ngiam,et al. Streaming Object Detection for 3-D Point Clouds , 2020, ECCV.
[3] Dong-Hyun Lee,et al. Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks , 2013 .
[4] Jiong Yang,et al. PointPillars: Fast Encoders for Object Detection From Point Clouds , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Quoc V. Le,et al. Rethinking Pre-training and Self-training , 2020, NeurIPS.
[6] ThrunSebastian,et al. Stanley: The robot that won the DARPA Grand Challenge , 2006 .
[7] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[8] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[9] David Berthelot,et al. MixMatch: A Holistic Approach to Semi-Supervised Learning , 2019, NeurIPS.
[10] Yin Zhou,et al. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[11] Bo Li,et al. SECOND: Sparsely Embedded Convolutional Detection , 2018, Sensors.
[12] Dragomir Anguelov,et al. Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection , 2020, CoRL.
[13] Tomas Pfister,et al. Learning from Simulated and Unsupervised Images through Adversarial Training , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Yan Wang,et al. Train in Germany, Test in the USA: Making 3D Object Detectors Generalize , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Tolga Tasdizen,et al. Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning , 2016, NIPS.
[16] Jianxiong Xiao,et al. SUN RGB-D: A RGB-D scene understanding benchmark suite , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] George Papandreou,et al. Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[18] Quoc V. Le,et al. Improved Noisy Student Training for Automatic Speech Recognition , 2020, INTERSPEECH.
[19] Fei-Fei Li,et al. OPTIMOL: Automatic Online Picture Collection via Incremental Model Learning , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[20] Yu Wang,et al. AFDet: Anchor Free One Stage 3D Object Detection , 2020, ArXiv.
[21] A. Krizhevsky. Convolutional Deep Belief Networks on CIFAR-10 , 2010 .
[22] Dumitru Erhan,et al. Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Benjamin Recht,et al. Do ImageNet Classifiers Generalize to ImageNet? , 2019, ICML.
[24] Bin Yang,et al. HDNET: Exploiting HD Maps for 3D Object Detection , 2018, CoRL.
[25] Yue Wang,et al. Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection , 2020, ArXiv.
[26] Kan Chen,et al. Billion-scale semi-supervised learning for image classification , 2019, ArXiv.
[27] David Berthelot,et al. FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence , 2020, NeurIPS.
[28] Awni Hannun,et al. Self-Training for End-to-End Speech Recognition , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[29] Matthias Nießner,et al. ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Luc Van Gool,et al. Weakly Supervised 3D Object Detection from Lidar Point Cloud , 2020, ECCV.
[31] Quoc V. Le,et al. Improving 3D Object Detection through Progressive Population Based Augmentation , 2020, ECCV.
[32] Bin Yang,et al. Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[33] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[34] Fabio Roli,et al. Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning , 2018, CCS.
[35] Bin Yang,et al. PIXOR: Real-time 3D Object Detection from Point Clouds , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[36] H. J. Scudder,et al. Probability of error of some adaptive pattern-recognition machines , 1965, IEEE Trans. Inf. Theory.
[37] Harri Valpola,et al. Weight-averaged consistency targets improve semi-supervised deep learning results , 2017, ArXiv.
[38] David Berthelot,et al. ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring , 2019, ArXiv.
[39] Yu Wang,et al. 1st Place Solution for Waymo Open Dataset Challenge - 3D Detection and Domain Adaptation , 2020, ArXiv.
[40] Martial Hebert,et al. Semi-Supervised Self-Training of Object Detection Models , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.
[41] Raquel Urtasun,et al. Auto4D: Learning to Label 4D Objects from Sequential Point Clouds , 2021, ArXiv.
[42] Thomas G. Dietterich,et al. Benchmarking Neural Network Robustness to Common Corruptions and Perturbations , 2018, ICLR.
[43] Yin Zhou,et al. End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds , 2019, CoRL.
[44] Andreas Geiger,et al. Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..
[45] Lei Zhang,et al. Structure Aware Single-Stage 3D Object Detection From Point Cloud , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[47] Sammy Omari,et al. One Thousand and One Hours: Self-driving Motion Prediction Dataset , 2020, CoRL.
[48] Quoc V. Le,et al. Self-Training With Noisy Student Improves ImageNet Classification , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[49] Dragomir Anguelov,et al. Scalability in Perception for Autonomous Driving: Waymo Open Dataset , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[50] G. McLachlan. Iterative Reclassification Procedure for Constructing An Asymptotically Optimal Rule of Allocation in Discriminant-Analysis , 1975 .
[51] Yin Zhou,et al. StarNet: Targeted Computation for Object Detection in Point Clouds , 2019, ArXiv.
[52] Leonidas J. Guibas,et al. 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[53] Xianzhi Li,et al. PointAugment: An Auto-Augmentation Framework for Point Cloud Classification , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[54] Han Zhang,et al. A Simple Semi-Supervised Learning Framework for Object Detection , 2020, ArXiv.
[55] Maxwell D. Collins,et al. Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation , 2020, ArXiv.
[56] Qiang Xu,et al. nuScenes: A Multimodal Dataset for Autonomous Driving , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).