[37] Zhizhong Han, Xiyang Wang, Yu-Shen Liu, and Matthias Zwicker. Multi-angle point cloudvae: Unsupervised feature learning for 3d point clouds from multiple angles by joint self-reconstruction and half-to-half prediction. In IEEE International Conference on Computer Vision, pages 10441-10450, 2019.
[38] Shidi Li, Miaomiao Liu, and Christian Walder. Editvae: Unsupervised parts-aware controllable 3d point cloud shape generation. In AAAI Conference on Artificial Intelligence, volume 36, pages 1386-1394, 2022.
[39] Pulak Purkait, Christopher Zach, and Ian Reid. Sg-vae: Scene grammar variational autoencoder to generate new indoor scenes. In European Conference on Computer Vision, pages 155–171, 2020.
[40] Adam R Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Sona Mokrá, and Danilo Jimenez Rezende. Nerf-vae: A geometry aware 3d scene generative model. In International Conference on Machine Learning, pages 5742-5752, 2021.
[41] Xin Wang, Thomas E Huang, Trevor Darrell, Joseph E Gonzalez, and Fisher Yu. Frustratingly simple few-shot object detection. arXiv preprint arXiv:2003.06957, 2020.
[42] Jiaxi Wu, Songtao Liu, Di Huang, and Yunhong Wang. Multi-scale positive sample refinement for few-shot object detection. In European Conference on Computer Vision, pages 456-472, 2020.
[43] Weilin Zhang and Yu-Xiong Wang. Hallucination improves few-shot object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 13008-13017, 2021.
[44] Bo Sun, Banghuai Li, Shengcai Cai, Ye Yuan, and Chi Zhang. Fsce: Few-shot object detection via contrastive proposal encoding. In IEEE Conference on Computer Vision and Pattern Recognition, pages 7352-7362, 2021.
[45] Bingyi Kang, Zhuang Liu, Xin Wang, Fisher Yu, Jiashi Feng, and Trevor Darrell. Few-shot object detection via feature reweighting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8420-8429, 2019.
[46] Guangxing Han, Shiyuan Huang, Jiawei Ma, Yicheng He, and Shih-Fu Chang. Meta faster r-cnn: Towards accurate few-shot object detection with attentive feature alignment. In AAAI Conference on Artificial Intelligence, volume 36, pages 780-789, 2022.
[47] Qi Fan, Wei Zhuo, Chi-Keung Tang, and Yu-Wing Tai. Few-shot object detection with attention-rpn and multi-relation detector. In IEEE Conference on Computer Vision and Pattern Recognition, pages 4013–4022, 2020.
[48] Xiaopeng Yan, Ziliang Chen, Anni Xu, Xiaoxi Wang, Xiaodan Liang, and Liang Lin. Meta R-CNN: Towards general solver for instance-level low-shot learning. In IEEE International Conference on Computer Vision, pages 9577-9586, 2019.
[49] Guangxing Han, Yicheng He, Shiyuan Huang, Jiawei Ma, and Shih-Fu Chang. Query adaptive few-shot object detection with heterogeneous graph convolutional networks. In IEEE International Conference on Computer Vision, pages 3263-3272, 2021.
[50] Carl Doersch, Ankush Gupta, and Andrew Zisserman. Crosstransformers: spatially-aware few-shot transfer. In Advances in Neural Information Processing Systems, volume 33, pages 21981-21993, 2020.
[51] Tung-I Chen, Yueh-Cheng Liu, Hung-Ting Su, Yu-Cheng Chang, Yu-Hsiang Lin, Jia-Fong Yeh, Wen-Chin Chen, and Winston Hsu. Dual-awareness attention for few-shot object detection. IEEE Transactions on Multimedia, 2021.
[52] Ding-Jie Chen, He-Yen Hsieh, and Tyng-Luh Liu. Adaptive image transformer for one-shot object detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 12247-12256, 2021.