[32] Deepak Pathak, Ross Girshick, Piotr Dólár, Trevor Darrell, and Bharath Hariharan. Learning features by watching objects move. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2701-2710, 2017. 6
[33] André Susano Pinto, Alexander Kolesnikov, Yuge Shi, Lucas Beyer, and Xiaohua Zhai. Tuning computer vision models with task rewards. arXiv preprint arXiv:2302.08242, 2023. 2, 3
[34] Charles R Qi, Or Litany, Kaiming He, and Leonidas J Guibas. Deep hough voting for 3d object detection in point clouds. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9277–9286, 2019. 2
[35] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In International Conference on Machine Learning, pages 8821-8831. PMLR, 2021. 3
[36] Zhile Ren and Gregory Shakhnarovich. Image segmentation by cascaded region agglomeration. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2011-2018, 2013. 3
[37] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022. 3
[38] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. 3
[39] Shaoshuai Shi, Chaoxu Guo, Li Jiang, Zhe Wang, Jianping Shi, Xiaogang Wang, and Hongsheng Li. Pv-rcnn: Point-voxel feature set abstraction for 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10529–10538, 2020. 2
[40] Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li. Pointcnn: 3d object proposal generation and detection from point cloud. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 770-779, 2019. 1, 2, 5, 6
[41] Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. Learning to summarize with human feedback. Advances in Neural Information Processing Systems, 33:3008-3021, 2020. 3
[42] OpenPCDet Development Team. Openpcdet: An open-source toolbox for 3d object detection clouds. https://github.com/open-mmlab/OpenPCDet, 2020.6
[43] Hao Tian, Yuntao Chen, Jifeng Dai, Zhaoxiang Zhang, and Xizhou Zhu. Unsupervised object detection with lidar clues. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5962-5972, 2021. 3
[44] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Roziere, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 3
[45] Jasper RR Uijlings, Koen EA Van De Sande, Theo Gevers, and Arnold WM Smeulders. Selective search for object recognition. International journal of computer vision, 104:154-171, 2013. 3
[46] Chen Wang, Junfeng Ding, Xiangyu Chen, Zelin Ye, Jialu Wang, Ziruo Cai, and Cewu Lu. Tendencycyrl: Multi-stage discriminative hints for efficient goal-oriented reverse curriculum learning. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3474-3480. IEEE, 2019. 5
[47] Yan Wang, Xiangyu Chen, Yurong You, Li Erran Li, Bharath Hariharan, Mark Campbell, Kilian Q Weinberger, and Wei-Lun Chao. Train in germany, test in the usa: Making 3d object detectors generalize. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11713- 11723, 2020. 3, 4
[48] Yangtao Wang, Xi Shen, Shell Xu Hu, Yuan Yuan, James L Crowley, and Dominique Vaufreydaz. Self-supervised transformers for unsupervised object discovery using normalized cut. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14543-14553, 2022. 3
[49] Yuqi Wang, Yuntao Chen, and Zhaoxiang Zhang. 4d unsupervised object discovery. arXiv preprint arXiv:2210.04801, 2022.3