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The curation of large-scale datasets is still costly and requires much time and resources.
S. Song, S. P. Lichtenberg, and J. Xiao, “Sun rgb-d: A rgb-d scene understanding benchmark suite,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 567–576
2015
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S. Lefevre, A. Carvalho, and F. Borrelli, “A learning-based framework for velocity control in autonomous driving,” IEEE Transactions on Automation Science and Engineering , vol. 13, no. 1, pp. 32–42, 2015
2015
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F. Dayoub, N. Sunderhauf, and P. I. Corke, “Episode-based active learning with bayesian neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2017, pp. 26–28
2017
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T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2117–2125
2017
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F. Dayoub, N. Sünderhauf, and P. Corke, “Episode-based active learning with bayesian neural networks,” 2017
2017
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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
2018
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O. Sener and S. Savarese, “Active learning for convolutional neural networks: A core-set approach,” 2018
2018
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D. Feng, X. Wei, L. Rosenbaum, A. Maki, and K. Dietmayer, “Deep active learning for efficient training of a lidar 3d object detector,” in 2019 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2019, pp. 667–674
2019
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M. Meyer and G. Kuschk, “Automotive radar dataset for deep learning based 3d object detection,” in 2019 16th european radar conference (EuRAD) . IEEE, 2019, pp. 129–132
2019
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W. Zimmer, A. Rangesh, and M. Trivedi, “3d bat: A semi-automatic, web-based 3d annotation toolbox for full-surround, multi-modal data streams,” in 2019 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2019, pp. 1816–1821
2019
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G. Villalonga and A. M. L. Pena, “Co-training for on-board deep object detection,” IEEE Access , vol. 8, pp. 194 441–194 456, 2020
2020
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S. Schmidt, Q. Rao, J. Tatsch, and A. Knoll, “Advanced active learning strategies for object detection,” in 2020 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2020, pp. 871–876
2020
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O. Çatal, S. Leroux, C. De Boom, T. Verbelen, and B. Dhoedt, “Anomaly detection for autonomous guided vehicles using bayesian surprise,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 8148–8153
2020
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S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang, and H. 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 , 2020, pp. 10 529–10 538
2020
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H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 621–11 631
2020
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Y. Xiao, Z. Chang, and B. Liu, “An efficient active learning method for multi-task learning,” Knowledge-Based Systems , vol. 190, p. 105137, 2020
2020
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J. T. Ash, C. Zhang, A. Krishnamurthy, J. Langford, and A. Agarwal, “Deep batch active learning by diverse, uncertain gradient lower bounds,” 2020
2020
Cited alongside, same era.
Q. Meng, W. Wang, T. Zhou, J. Shen, Y. Jia, and L. Van Gool, “Towards a weakly supervised framework for 3d point cloud object detection and annotation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 8, pp. 4454–4468, 2021
2021
Cited alongside, same era.
R. Greer, N. Deo, and M. Trivedi, “Trajectory prediction in autonomous driving with a lane heading auxiliary loss,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 4907–4914, 2021
2021
Cited alongside, same era.
H.-k. Chiu, J. Li, R. Ambruş, and J. Bohg, “Probabilistic 3d multi-modal, multi-object tracking for autonomous driving,” in 2021 IEEE international conference on robotics and automation (ICRA) . IEEE, 2021, pp. 14 227–14 233
2021
Cited alongside, same era.
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, pp. 15 599–15 608
2023
Later among the works it cites.
A. Almin, L. Lemarié, A. Duong, and B. R. Kiran, “Navya3dseg-navya 3d semantic segmentation dataset design & split generation for autonomous vehicles,” IEEE Robotics and Automation Letters , 2023
2023
Later among the works it cites.
A. Hekimoglu, P. Friedrich, W. Zimmer, M. Schmidt, A. Marcos-Ramiro, and A. Knoll, “Multi-task consistency for active learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 3415–3424
2023
Later among the works it cites.
A. A. Ghita, “Active learning for 3d object detection and labeling,” Master’s Thesis, Technical University of Munich, TUM School of Computation, Information and Technology, Munich, December 2023. [Online]. Available: https://www.ce.cit.tum.de/fileadmin/w00cgn/air/Personal_Files/WalterZimmer/final_masters_thesis_ahmed_ghita_compressed_default.pdf
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J. Huang, P. K. Choudhury, S. Yin, and L. Zhu, “Real-time road curb and lane detection for autonomous driving using lidar point clouds,” IEEE Access , vol. 9, pp. 144 940–144 951, 2021
2021
Cited alongside, same era.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 10 012–10 022
2021
Cited alongside, same era.
A. Moses, S. Jakkampudi, C. Danner, and D. Biega, “Localization-based active learning (local) for object detection in 3d point clouds,” in Geospatial Informatics XII , vol. 12099. SPIE, 2022, pp. 44–58
2022
Cited alongside, same era.
L. Chen, X. He, X. Zhao, H. Li, Y. Huang, B. Zhou, W. Chen, Y. Li, C. Wen, and C. Wang, “Gocomfort: Comfortable navigation for autonomous vehicles leveraging high-precision road damage crowdsensing,” IEEE Transactions on Mobile Computing , 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Y. Luo, Z. Chen, Z. Wang, X. Yu, Z. Huang, and M. Baktashmotlagh, “Exploring active 3d object detection from a generalization perspective,” in The Eleventh International Conference on Learning Representations , 2022
2022
Cited alongside, same era.
A. Hekimoglu, M. Schmidt, A. Marcos-Ramiro, and G. Rigoll, “Efficient active learning strategies for monocular 3d object detection,” in 2022 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2022, pp. 295–302
2022
Cited alongside, same era.
R. Greer, J. Isa, N. Deo, A. Rangesh, and M. M. Trivedi, “On salience-sensitive sign classification in autonomous vehicle path planning: Experimental explorations with a novel dataset,” in 2022 Winter Conference on Applications of Computer Vision (WACV)
2022
Cited alongside, same era.
2023
Later among the works it cites.
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 2023 IEEE international conference on robotics and automation (ICRA) . IEEE, 2023, pp. 2774–2781
2023
Later among the works it cites.
W. Zimmer, C. Creß, H. T. Nguyen, and A. C. Knoll, “Tumtraf intersection dataset: All you need for urban 3d camera-lidar roadside perception,” in 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023) . IEEE, 2023
2023
Later among the works it cites.
R. Greer, A. Gopalkrishnan, N. Deo, A. Rangesh, and M. Trivedi, “Salient sign detection in safe autonomous driving: Ai which reasons over full visual context,” 27th International Technical Symposium on the Enhanced Safety of Vehicles (ESV) , 2023
2023
Later among the works it cites.
R. Greer, A. Gopalkrishnan, J. Landgren, L. Rakla, A. Gopalan, and M. Trivedi, “Robust traffic light detection using salience-sensitive loss: Computational framework and evaluations,” in 2023 IEEE Intelligent Vehicles Symposium (IV) , 2023, pp. 1–7
2023
Later among the works it cites.
W. Zimmer, J. Wu, X. Zhou, and A. C. Knoll, “Real-time and robust 3d object detection with roadside lidars,” in Proceedings of the 12th International Scientific Conference on Mobility and Transport: Mobility Innovations for Growing Megacities . Springer, 2023, pp. 199–219
2023
Later among the works it cites.
W. Zimmer, J. Birkner, M. Brucker, H. T. Nguyen, S. Petrovski, B. Wang, and A. C. Knoll, “Infradet3d: Multi-modal 3d object detection based on roadside infrastructure camera and lidar sensors,” in 2023 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Luo, Z. Chen, Z. Wang, X. Yu, Z. Huang, and M. Baktashmotlagh, “Exploring active 3d object detection from a generalization perspective,” in The Eleventh International Conference on Learning Representations , 2023. [Online]. Available: https://openreview.net/forum?id=2RwXVje1rAh
2023
Later among the works it cites.
A. Hekimoglu, M. Schmidt, and A. Marcos-Ramiro, “Monocular 3d object detection with lidar guided semi supervised active learning,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 2346–2355
2024
Closest in time.
R. Greer, A. Gopalkrishnan, M. Keskar, and M. M. Trivedi, “Patterns of vehicle lights: Addressing complexities of camera-based vehicle light datasets and metrics,” Pattern Recognition Letters , 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167865524000047
2024
Closest in time.