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Due to its robust and precise distance measurements, LiDAR plays an important role in scene understanding for autonomous driving.
Unsupervised Domain Adaptation through Self-Supervision
Sun, Y.; Tzeng, E.; Darrell, T.; and Efros, A. A. 2019 · 1909
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CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Hoffman, J.; Tzeng, E.; Park, T.; Zhu, J.-Y.; Isola, P.; Saenko, K.; Efros, A. A.; and Darrell, T. 2018 · 2003
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LiDARNet: A Boundary-Aware Domain Adaptation Model for Lidar Point Cloud Semantic Segmentation
Jiang, P.; and Saripalli, S. 2020 · 2003
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Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A.; Lenz, P.; and Urtasun, R. 2012 · 2012
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Generative adversarial nets
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014 · 2014
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S.; and Szegedy, C. 2015 · 2015
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Learning transferable features with deep adaptation networks
Long, M.; Cao, Y.; Wang, J.; and Jordan, M. 2015 · 2015
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Visual domain adaptation: A survey of recent advances
Patel, V. M.; Gopalan, R.; Li, R.; and Chellappa, R. 2015 · 2015
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Ba, J. L.; Kiros, J. R.; and Hinton, G. E. 2016 · 2016
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R-fcn: Object detection via region-based fully convolutional networks
Dai, J.; Li, Y.; He, K.; and Sun, J. 2016 · 2016
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Playing for data: Ground truth from computer games
Richter, S. R.; Vineet, V.; Roth, S.; and Koltun, V. 2016 · 2016
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Deep CORAL: Correlation Alignment for Deep Domain Adaptation
Sun, B.; and Saenko, K. 2016 · 2016
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Instance normalization: The missing ingredient for fast stylization
Ulyanov, D.; Vedaldi, A.; and Lempitsky, V. 2016 · 2016
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Multi-scale context aggregation by dilated convolutions
Yu, F.; and Koltun, V. 2016 · 2016
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Fast LIDAR-based road detection using fully convolutional neural networks
Caltagirone, L.; Scheidegger, S.; Svensson, L.; and Wahde, M. 2017 · 2017
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Deformable convolutional networks
Dai, J.; Qi, H.; Xiong, Y.; Li, Y.; Zhang, G.; Hu, H.; and Wei, Y. 2017 · 2017
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CARLA: An open urban driving simulator
Dosovitskiy, A.; Ros, G.; Codevilla, F.; Lopez, A.; and Koltun, V. 2017 · 2017
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Escape from cells: Deep kd-networks for the recognition of 3d point cloud models
Klokov, R.; and Lempitsky, V. 2017 · 2017
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Focal loss for dense object detection
Lin, T.-Y.; Goyal, P.; Girshick, R.; He, K.; and Dollár, P. 2017 · 2017
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Learning from simulated and unsupervised images through adversarial training
Shrivastava, A.; Pfister, T.; Tuzel, O.; Susskind, J.; Wang, W.; and Webb, R. 2017 · 2017
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Adversarial discriminative domain adaptation
Tzeng, E.; Hoffman, J.; Saenko, K.; and Darrell, T. 2017 · 2017
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O-cnn: Octree-based convolutional neural networks for 3d shape analysis
Wang, P.-S.; Liu, Y.; Guo, Y.-X.; Sun, C.-Y.; and Tong, X. 2017 · 2017
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Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks
Zhu, J.-Y.; Park, T.; Isola, P.; and Efros, A. A. 2017 · 2017
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Deep Unsupervised Convolutional Domain Adaptation
Zhuo, J.; Wang, S.; Zhang, W.; and Huang, Q. 2017 · 2017
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Recurrent slice networks for 3d segmentation of point clouds
Huang, Q.; Wang, W.; and Neumann, U. 2018 · 2018
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Free supervision from video games
Krähenbühl, P. 2018 · 2018
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Large-scale point cloud semantic segmentation with superpoint graphs
Landrieu, L.; and Simonovsky, M. 2018 · 2018
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Drop to adapt: Learning discriminative features for unsupervised domain adaptation
Lee, S.; Kim, D.; Kim, N.; and Jeong, S.-G. 2019 · 2019
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Octree guided CNN with spherical kernels for 3D point clouds
Lei, H.; Akhtar, N.; and Mian, A. 2019 · 2019
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Scale-aware trident networks for object detection
Li, Y.; Chen, Y.; Wang, N.; and Zhang, Z. 2019 · 2019
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Interpolated convolutional networks for 3d point cloud understanding
Mao, J.; Wang, X.; and Li, H. 2019 · 2019
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VV-Net: Voxel vae net with group convolutions for point cloud segmentation
Meng, H.-Y.; Gao, L.; Lai, Y.-K.; and Manocha, D. 2019 · 2019
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Rangenet++: Fast and accurate lidar semantic segmentation
Milioto, A.; Vizzo, I.; Behley, J.; and Stachniss, C. 2019 · 2019
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Pointgrid: A deep network for 3d shape understanding
Le, T.; and Duan, Y. 2018 · 2018
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So-net: Self-organizing network for point cloud analysis
Li, J.; Chen, B. M.; and Hee Lee, G. 2018 · 2018
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Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation
Morerio, P.; Cavazza, J.; and Murino, V. 2018 · 2018
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From source to target and back: symmetric bi-directional adaptive gan
Russo, P.; Carlucci, F. M.; Tommasi, T.; and Caputo, B. 2018 · 2018
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Generate to adapt: Aligning domains using generative adversarial networks
Sankaranarayanan, S.; Balaji, Y.; Castillo, C. D.; and Chellappa, R. 2018 · 2018
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Rgcnn: Regularized graph cnn for point cloud segmentation
Te, G.; Hu, W.; Zheng, A.; and Guo, Z. 2018 · 2018
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Moment matching for multi-source domain adaptation
Peng, X.; Bai, Q.; Xia, X.; Huang, Z.; Saenko, K.; and Wang, B. 2019 · 2019
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PointDAN: A multi-scale 3D domain adaption network for point cloud representation
Qin, C.; You, H.; Wang, L.; Kuo, C.-C. J.; and Fu, Y. 2019 · 2019
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Cross-Sensor Deep Domain Adaptation for LiDAR Detection and Segmentation
Rist, C. B.; Enzweiler, M.; and Gavrila, D. M. 2019 · 2019
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Domain Adaptation for Vehicle Detection from Bird’s Eye View LiDAR Point Cloud Data
Saleh, K.; Abobakr, A.; Attia, M.; Iskander, J.; Nahavandi, D.; Hossny, M.; and Nahvandi, S. 2019 · 2019
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Learning to generate synthetic data via compositing
Tripathi, S.; Chandra, S.; Agrawal, A.; Tyagi, A.; Rehg, J. M.; and Chari, V. 2019 · 2019
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Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud
Wu, B.; Zhou, X.; Zhao, S.; Yue, X.; and Keutzer, K. 2019 · 2019
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Shellnet: Efficient point cloud convolutional neural networks using concentric shells statistics
Zhang, Z.; Hua, B.-S.; and Yeung, S.-K. 2019 · 2019
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HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation
Chen, C.; Fu, Z.; Chen, Z.; Jin, S.; Cheng, Z.; Jin, X.; and Hua, X.-S. 2020 · 2020
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Multi-Source Domain Adaptation for Visual Sentiment Classification
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Self-Supervised Learning for Domain Adaptation on Point Clouds
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Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources
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Return of frustratingly easy domain adaptation
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