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Reducing the quantity of annotations required for supervised training is vital when labels are scarce and costly.
ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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A category-level 3-D object dataset: Putting the kinect to work
A. Janoch, S. Karayev, Y. Jia, J. T. Barron, M. Fritz, K. Saenko, and T. Darrell · 2011
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Indoor segmentation and support inference from RGBD images
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus · 2012
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Are we ready for autonomous driving? The KITTI vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
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SUN3D: A database of big spaces reconstructed using SfM and object labels
J. Xiao, A. Owens, and A. Torralba · 2013
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SUNRGBD: A RGB-D scene understanding benchmark suite
S. Song, S. P. Lichtenberg, and J. Xiao · 2015
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3D semantic parsing of large-scale indoor spaces
I. Armeni, O. Sener, A. R. Zamir, H. Jiang, I. Brilakis, M. Fischer, and S. Savarese · 2016
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Mask R-CNN
K. He, G. Gkioxari, P. Dollar, and R. Girshick · 2017
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PointNet: Deep learning on point sets for 3D classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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ScanNet: Richly-annotated 3D reconstructions of indoor scenes
A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner · 2017
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Representation learning with contrastive predictive coding
A. v. d. Oord, Y. Li, and O. Vinyals · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Z. Wu, Y. Xiong, S. X. Yu, and D. Lin · 2018
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4D spatio-temporal ConvNets: Minkowski convolutional neural networks
C. Choy, J. Gwak, and S. Savarese · 2019
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SemanticKITTI: A dataset for semantic scene understanding of lidar sequences
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Digging into self-supervised monocular depth estimation
C. Godard, O. M. Aodha, M. Firman, and G. Brostow · 2019
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C. Choy, J. Park, and V. Koltun · 2019
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PointGroup: Dual-set point grouping for 3D instance segmentation
L. Jiang, H. Zhao, S. Shi, S. Liu, C.-W. Fu, and J. Jia · 2020
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PointContrast: Unsupervised pre-training for 3D point cloud understanding
S. Xie, J. Gu, D. Guo, C. R. Qi, L. J. Guibas, and O. Litany · 2020
Self-supervised pretraining of 3D features on any point-cloud
Z. Zhang, R. Girdhar, A. Joulin, and I. Misra · 2021
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Exploring data-efficient 3D scene understanding with contrastive scene contexts
J. Hou, B. Graham, M. Nießner, and S. Xie · 2021
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Exploring simple Siamese representation learning
X. Chen and K. He · 2021
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Prototypical contrastive learning of unsupervised representations
J. Li, P. Zhou, C. Xiong, and S. C. H. Hoi · 2021
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Dense contrastive learning for self-supervised visual pre-training
X. Wang, R. Zhang, C. Shen, T. Kong, and L. Li · 2021
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Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning
Z. Xie, Y. Lin, Z. Zhang, Y. Cao, S. Lin, and H. Hu · 2021
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A simple framework for contrastive learning of visual representations
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Momentum contrast for unsupervised visual representation learning
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Bootstrap your own latent - A new approach to self-supervised learning
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar, B. Piot, k. kavukcuoglu, R. Munos, and M. Valko · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
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Supervised contrastive learning
P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan · 2020
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Exploring cross-image pixel contrast for semantic segmentation
W. Wang, T. Zhou, F. Yu, J. Dai, E. Konukoglu, and L. V. Gool · 2021
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Pri3D: Can 3D priors help 2D representation learning?
J. Hou, S. Xie, B. Graham, A. Dai, and M. Nießner · 2021
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SimIPU: Simple 2D image and 3D point cloud unsupervised pre-training for spatial-aware visual representations
Z. Li, Z. Chen, A. Li, L. Fang, Q. Jiang, X. Liu, J. Jiang, B. Zhou, and H. Zhao · 2022
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CrossPoint: Self-supervised cross-modal contrastive learning for 3D point cloud understanding
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Image-to-lidar self-supervised distillation for autonomous driving data
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