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3D object detection has recently received much attention due to its great potential in autonomous vehicle (AV).
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2018
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Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4490–4499
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T. Yin, X. Zhou, and P. Krahenbuhl, “Center-based 3d object detection and tracking,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 11 784–11 793
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A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019
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D. Yoo and I. S. Kweon, “Learning loss for active learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 93–102
2019
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2019
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Y.-P. Tang and S.-J. Huang, “Self-paced active learning: Query the right thing at the right time,” in Proceedings of the AAAI conference on artificial intelligence , vol. 33, no. 01, 2019, pp. 5117–5124
2019
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S. Sinha, S. Ebrahimi, and T. Darrell, “Variational adversarial active learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 5972–5981
2019
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2019
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Z. Wang and K. Jia, “Frustum convnet: Sliding frustums to aggregate local point-wise features for amodal 3d object detection,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 1742–1749
2019
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T. Yuan, F. Wan, M. Fu, J. Liu, S. Xu, X. Ji, and Q. Ye, “Multiple instance active learning for object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 5330–5339
2021
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2021
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T.-H. Wu, Y.-C. Liu, Y.-K. Huang, H.-Y. Lee, H.-T. Su, P.-C. Huang, and W. H. Hsu, “Redal: Region-based and diversity-aware active learning for point cloud semantic segmentation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021
2021
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J. Wu, J. Chen, and D. Huang, “Entropy-based active learning for object detection with progressive diversity constraint,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022
2022
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W. Yu, S. Zhu, T. Yang, and C. Chen, “Consistency-based active learning for object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022
2022
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S. Kothawade, S. Ghosh, S. Shekhar, Y. Xiang, and R. Iyer, “Talisman: targeted active learning for object detection with rare classes and slices using submodular mutual information,” in European Conference on Computer Vision , 2022
2022
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M. Lyu, J. Zhou, H. Chen, Y. Huang, D. Yu, Y. Li, Y. Guo, Y. Guo, L. Xiang, and G. Ding, “Box-level active detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2023, pp. 23 766–23 775
2023
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Y. Park, W. Choi, S. Kim, D.-J. Han, and J. Moon, “Active learning for object detection with evidential deep learning and hierarchical uncertainty aggregation,” in The Eleventh International Conference on Learning Representations , 2023
2023
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J. Yuan, B. Zhang, X. Yan, T. Chen, B. Shi, Y. Li, and Y. Qiao, “Bi3d: Bi-domain active learning for cross-domain 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023
2023
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Z. Liu, H. Tang, A. Amini, X. Yang, H. Mao, D. L. Rus, and S. Han, “Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation,” in IEEE International Conference on Robotics and Automation , 2023
2023
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D. Fuchsgruber, T. Wollschläger, B. Charpentier, A. Oroz, and S. Günnemann, “Uncertainty for active learning on graphs,” 2024
2024
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B. Safaei, V. Vibashan, C. M. de Melo, and V. M. Patel, “Entropic open-set active learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 5, 2024, pp. 4686–4694
2024
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X. Li, P. Yang, Y. Gu, X. Zhan, T. Wang, M. Xu, and C. Xu, “Deep active learning with noise stability,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 12, 2024, pp. 13 655–13 663
2024
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Z. Li, X. Xu, S. Lim, and H. Zhao, “Unimode: Unified monocular 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 16 561–16 570
2024
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Z. Li, S. Lan, J. M. Alvarez, and Z. Wu, “Bevnext: Reviving dense bev frameworks for 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 20 113–20 123
2024
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Yang, Chenhongyi and Huang, Lichao and Crowley, Elliot J., “Plug and Play Active Learning for Object Detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2024
2024
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Y. He, L. Cai, J. Liao, and C.-S. Foo, “Hybrid active learning with uncertainty-weighted embeddings,” Transactions on Machine Learning Research , 2024
2024
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Y. Li and Y. Gal, “Dropout inference in bayesian neural networks with alpha-divergences,” in International conference on machine learning . PMLR, 2017, pp. 2052–2061
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