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Autonomous robot navigation within the dynamic unknown environment is of crucial significance for mobile robotic applications including robot navigation in last-mile delivery and robot-enabled automated supplies in industrial and hospital delivery applications.
S. J. Kwon, D. Lee, B. Kim, P. Kapoor, B. Park, and G.-Y. Wei, “Structured compression by weight encryption for unstructured pruning and quantization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 1909–1918
1918
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Advances in neural information processing systems , vol. 28, 2015
2015
Earlier work this paper cites.
I. Armeni, O. Sener, A. R. Zamir, H. Jiang, I. Brilakis, M. Fischer, and S. Savarese, “3d semantic parsing of large-scale indoor spaces,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 1534–1543
2016
Earlier work this paper cites.
P. Sharma, N. Ding, S. Goodman, and R. Soricut, “Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2018, pp. 2556–2565
2018
Earlier work this paper cites.
K. Liu, X. Han, and B. M. Chen, “Deep learning based automatic crack detection and segmentation for unmanned aerial vehicle inspections,” in 2019 IEEE international conference on robotics and biomimetics (ROBIO) . IEEE, 2019, pp. 381–387
2019
Earlier work this paper cites.
L. Yang, Y. Fan, and N. Xu, “Video instance segmentation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 5188–5197
2019
Earlier work this paper cites.
Z. Tian, C. Shen, H. Chen, and T. He, “Fcos: Fully convolutional one-stage object detection,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 9627–9636
2019
Earlier work this paper cites.
A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan et al. , “Searching for mobilenetv3,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 1314–1324
2019
Earlier work this paper cites.
M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International conference on machine learning . PMLR, 2019, pp. 6105–6114
2019
Earlier work this paper cites.
K. Liu, Z. Gao, F. Lin, and B. M. Chen, “Fg-net: Fast large-scale lidar point clouds understanding network leveraging correlated feature mining and geometric-aware modelling,” IEEE Transactions on Cybernetics , 2020
2020
Earlier work this paper cites.
H. Tanaka, D. Kunin, D. L. Yamins, and S. Ganguli, “Pruning neural networks without any data by iteratively conserving synaptic flow,” Advances in neural information processing systems , vol. 33, pp. 6377–6389, 2020
2020
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
R. Dale, “Gpt-3: What’s it good for?” Natural Language Engineering , vol. 27, no. 1, pp. 113–118, 2021
2021
Earlier work this paper cites.
T. Zhang, S. Ye, X. Feng, X. Ma, K. Zhang, Z. Li, J. Tang, S. Liu, X. Lin, Y. Liu et al. , “Structadmm: Achieving ultrahigh efficiency in structured pruning for dnns,” IEEE transactions on neural networks and learning systems , vol. 33, no. 5, pp. 2259–2273, 2021
2021
Earlier work this paper cites.
T. Liang, J. Glossner, L. Wang, S. Shi, and X. Zhang, “Pruning and quantization for deep neural network acceleration: A survey,” Neurocomputing , vol. 461, pp. 370–403, 2021
2021
Cited alongside, same era.
K. Li, M. Li, and U. D. Hanebeck, “Towards high-performance solid-state-lidar-inertial odometry and mapping,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 5167–5174, 2021
2021
Cited alongside, same era.
K. Liu, “Rm3d: Robust data-efficient 3d scene parsing via traditional and learnt 3d descriptors-based semantic region merging,” International Journal of Computer Vision , vol. 131, no. 4, pp. 938–967, 2022
2022
Cited alongside, same era.
K. Liu and B. M. Chen, “Industrial uav-based unsupervised domain adaptive crack recognitions: From database towards real-site infrastructural inspections,” IEEE Transactions on Industrial Electronics , vol. 70, no. 9, pp. 9410–9420, 2022
2022
Cited alongside, same era.
S. Liang, H. Wu, L. Zhen, Q. Hua, S. Garg, G. Kaddoum, M. M. Hassan, and K. Yu, “Edge yolo: Real-time intelligent object detection system based on edge-cloud cooperation in autonomous vehicles,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 12, pp. 25 345–25 360, 2022
2022
Later among the works it cites.
K. Liu, A. Xiao, X. Zhang, S. Lu, and L. Shao, “Fac: 3d representation learning via foreground aware feature contrast,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9476–9485
2023
Later among the works it cites.
K. Liu and M. Cao, “Dlc-slam: A robust lidar-slam system with learning-based denoising and loop closure,” IEEE/ASME Transactions on Mechatronics , 2023
2023
Later among the works it cites.
2023
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K. Liu, Y. Zhao, Q. Nie, Z. Gao, and B. M. Chen, “Weakly supervised 3d scene segmentation with region-level boundary awareness and instance discrimination,” in European Conference on Computer Vision . Springer, 2022, pp. 37–55
2022
Cited alongside, same era.
——, “Ws3d supplementary material,” in European Conference on Computer Vision (ECCV). Springer, Cham , 2022, pp. 37–55
2022
Cited alongside, same era.
K. Liu, “A robust and efficient lidar-inertial-visual fused simultaneous localization and mapping system with loop closure,” in 2022 12th international conference on CYBER technology in automation, control, and intelligent systems (CYBER) . IEEE, 2022, pp. 1182–1187
2022
Cited alongside, same era.
K. Liu, A. Xiao, J. Huang, K. Cui, Y. Xing, and S. Lu, “D-lc-nets: Robust denoising and loop closing networks for lidar slam in complicated circumstances with noisy point clouds,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 12 212–12 218
2022
Cited alongside, same era.
M. Shen, P. Molchanov, H. Yin, and J. M. Alvarez, “When to prune? a policy towards early structural pruning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 12 247–12 256
2022
Cited alongside, same era.
K. Liu, X. Zhou, and B. M. Chen, “An enhanced lidar inertial localization and mapping system for unmanned ground vehicles,” in 2022 IEEE 17th International Conference on Control & Automation (ICCA) . IEEE, 2022, pp. 587–592
2022
Cited alongside, same era.
K. Liu and H. Ou, “A light-weight lidar-inertial slam system with high efficiency and loop closure detection capacity,” in 2022 International Conference on Advanced Robotics and Mechatronics (ARM) . IEEE, 2022, pp. 284–289
2022
Cited alongside, same era.
K. Liu, X. Zhou, B. Zhao, H. Ou, and B. M. Chen, “An integrated visual system for unmanned aerial vehicles following ground vehicles: Simulations and experiments,” in 2022 IEEE 17th International Conference on Control & Automation (ICCA) . IEEE, 2022, pp. 593–598
2022
Cited alongside, same era.
Later among the works it cites.
2023
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X. Sui, Q. Lv, L. Zhi, B. Zhu, Y. Yang, Y. Zhang, and Z. Tan, “A hardware-friendly high-precision cnn pruning method and its fpga implementation,” Sensors , vol. 23, no. 2, p. 824, 2023
2023
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2023
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G. Fang, X. Ma, M. Song, M. B. Mi, and X. Wang, “Depgraph: Towards any structural pruning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 16 091–16 101
2023
Later among the works it cites.
Z. Guo, H. Yan, H. Li, and X. Lin, “Class attention transfer based knowledge distillation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 11 868–11 877
2023
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2023
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X. Liu, B. Li, Z. Chen, and Y. Yuan, “Generalized gradient flow based saliency for pruning deep convolutional neural networks,” International Journal of Computer Vision , pp. 1–15, 2023
2023
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2024
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