[53] Guangxing Han, Jiawei Ma, Shiyuan Huang, Long Chen, and Shih-Fu Chang. Few-shot object detection with fully cross-transformer. In IEEE Conference on Computer Vision and Pattern Recognition, pages 5321-5330, 2022.
[54] Zhaoxin Fan, Hongyan Liu, Jun He, Qi Sun, and Xiaoyong Du. A graph-based one-shot learning method for point cloud recognition. In Computer Graphics Forum, volume 39, pages 313-323, 2020.
[55] Ali Cheraghian, Shafin Rahman, Dylan Campbell, and Lars Petersson. Transductive zero-shot learning for 3D point cloud classification. In IEEE Winter Conference on Applications of Computer Vision, pages 923–933, 2020.
[56] Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In Advances in Neural Information Processing Systems, volume 30, 2017.
[57] Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian. Foldingnet: Point cloud auto-encoder via deep grid deformation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 206–215, 2018.
[58] Charles R. Qi, Or Litany, Kaiming He, and Leonidas J. Guibas. Deep hough voting for 3d object detection in point clouds. In IEEE International Conference on Computer Vision, 2019.
[59] Jiawei Liu, Xingping Dong, Sanyuan Zhao, and Jianbing Shen. Generalized few-shot 3d object detection of lidar point cloud for autonomous driving. arXiv preprint arXiv:2302.03914, 2023.
[60] Saining Xie, Jiatao Gu, Demi Guo, Charles R Qi, Leonidas Guibas, and Or Litany. Pointcontrast: Unsupervised pre-training for 3d point cloud understanding. In European Conference on Computer Vision, pages 574-591, 2020.
[61] Ryosuke Yamada, Hirokatsu Kataoka, Naoya Chiba, Yukiyasu Domae, and Tetsuya Ogata. Point cloud pre-training with natural 3d structures. In IEEE Conference on Computer Vision and Pattern Recognition, pages 21283-21293, 2022.
[62] Shuaihang Yuan, Xiang Li, Hao Huang, and Yi Fang. Meta-det3d: Learn to learn few-shot 3d object detection. In Asian Conference on Computer Vision, pages 1761-1776, 2022.
[63] Xiongwei Wu, Doyen Sahoo, and Steven Hoi. Meta-rcnn: Meta learning for few-shot object detection. In ACM International Conference on Multimedia, pages 1679–1687, 2020.
[64] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In IEEE Conference on Computer Vision and Pattern Recognition, pages 248–255, 2009.
[65] Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J. Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In Advances in Neural Information Processing Systems, 2017.
[66] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652-660, 2017.
[67] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017.
[68] Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In IEEE Conference on Computer Vision and Pattern Recognition, 2017.