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Collaborative perception allows each agent to enhance its perceptual abilities by exchanging messages with others.
C. E. Shannon, “A mathematical theory of communication,” Bell Syst. Tech. J. , vol. 27, pp. 623–656, 1948
1948
Earlier work this paper cites.
D. A. Huffman, “A method for the construction of minimum-redundancy codes,” Resonance , vol. 11, pp. 91–99, 1952
1952
Earlier work this paper cites.
R. Carnap and Y. Bar-Hillel, “An outline of a theory of semantic information,” RLE Technical Reports 247, Research Laboratory of Electronics, Massachusetts Institute of Technology., Cambridge MA, Oct. 1952 , 1952
1952
Earlier work this paper cites.
A. D. Wyner and J. Ziv, “The rate-distortion function for source coding with side information at the decoder,” IEEE Trans. Inf. Theory , vol. 22, pp. 1–10, 1976
1976
Earlier work this paper cites.
J. van Leeuwen, “On the construction of huffman trees,” in International Colloquium on Automata, Languages and Programming , 1976
1976
Earlier work this paper cites.
D. J. C. MacKay, “Information theory, inference, and learning algorithms,” IEEE Transactions on Information Theory , vol. 50, pp. 2544–2545, 2004
2004
Earlier work this paper cites.
D. Salomon, “Data compression: The complete reference, 3rd edition,” 2004
2004
Earlier work this paper cites.
T. M. Cover and J. A. Thomas, “Elements of information theory,” 2005
2005
Earlier work this paper cites.
K. Bernardin and R. Stiefelhagen, “Evaluating multiple object tracking performance: The clear mot metrics,” EURASIP Journal on Image and Video Processing , vol. 2008, pp. 1–10, 2008
2008
Earlier work this paper cites.
J. Masci, U. Meier, D. Cirecsan, and J. Schmidhuber, “Stacked convolutional auto-encoders for hierarchical feature extraction,” in Artificial Neural Networks and Machine Learning–ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I 21 . Springer, 2011, pp. 52–59
2011
Earlier work this paper cites.
J. Bao, P. Basu, M. Dean, C. Partridge, A. Swami, W. Leland, and J. A. Hendler, “Towards a theory of semantic communication,” 2011 IEEE Network Science Workshop , pp. 110–117, 2011
2011
Earlier work this paper cites.
S. P. Boyd, N. Parikh, E. K.-W. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Found. Trends Mach. Learn. , vol. 3, pp. 1–122, 2011
2011
Earlier work this paper cites.
J. Scherer, S. Yahyanejad, S. Hayat, E. Yanmaz, T. Andre, A. Khan, V. Vukadinovic, C. Bettstetter, H. Hellwagner, and B. Rinner, “An autonomous multi-uav system for search and rescue,” in Proceedings of the First Workshop on Micro Aerial Vehicle Networks, Systems, and Applications for Civilian Use , 2015, pp. 33–38
2015
Earlier work this paper cites.
D. Lahat, T. Adal, and C. Jutten, “Multimodal data fusion: An overview of methods, challenges, and prospects,” Proceedings of the IEEE , vol. 103, pp. 1449–1477, 2015
2015
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in Conference on robot learning . PMLR, 2017, pp. 1–16
2017
Earlier work this paper cites.
H. Xiang, R. Xu, and J. Ma, “Hm-vit: Hetero-modal vehicle-to-vehicle cooperative perception with vision transformer,” ICCV , 2023
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
N. Farsad, M. Rao, and A. J. Goldsmith, “Deep learning for joint source-channel coding of text,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pp. 2326–2330, 2018
2018
Earlier work this paper cites.
E. T. Alotaibi, S. S. Alqefari, and A. Koubaa, “Lsar: Multi-uav collaboration for search and rescue missions,” IEEE Access , vol. 7, pp. 55 817–55 832, 2019
2019
Earlier work this paper cites.
Q. Chen, S. Tang, Q. Yang, and S. Fu, “Cooper: Cooperative perception for connected autonomous vehicles based on 3d point clouds,” 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) , pp. 514–524, 2019
2019
Earlier work this paper cites.
D. B. Kurka and D. Gunduz, “Deepjscc-f: Deep joint source-channel coding of images with feedback,” IEEE Journal on Selected Areas in Information Theory , vol. 1, pp. 178–193, 2019
2019
Earlier work this paper cites.
X. Zhou, D. Wang, and P. Krähenbühl, “Objects as points,” in arXiv preprint arXiv:1904.07850 , 2019
2019
Earlier work this paper cites.
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 12 689–12 697, 2019
2019
Earlier work this paper cites.
T.-H. Wang, S. Manivasagam, M. Liang, B. Yang, W. Zeng, and R. Urtasun, “V2vnet: Vehicle-to-vehicle communication for joint perception and prediction,” in European Conference on Computer Vision . Springer, 2020, pp. 605–621
2020
Earlier work this paper cites.
Z. Li, A. V. Barenji, J. Jiang, R. Y. Zhong, and G. Xu, “A mechanism for scheduling multi robot intelligent warehouse system face with dynamic demand,” Journal of Intelligent Manufacturing , vol. 31, no. 2, pp. 469–480, 2020
2020
Cited alongside, same era.
Y.-C. Liu, J. Tian, N. Glaser, and Z. Kira, “When2com: Multi-agent perception via communication graph grouping,” in Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition , 2020, pp. 4106–4115
2020
Cited alongside, same era.
Y.-C. Liu, J. Tian, C.-Y. Ma, N. Glaser, C.-W. Kuo, and Z. Kira, “Who2com: Collaborative perception via learnable handshake communication,” in IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 6876–6883
2020
Cited alongside, same era.
T.-H. Wang, S. Manivasagam, M. Liang, B. Yang, W. Zeng, and R. Urtasun, “V2vnet: Vehicle-to-vehicle communication for joint perception and prediction,” in European Conference on Computer Vision . Springer, 2020, pp. 605–621
2020
2021
Later among the works it cites.
2021
Later among the works it cites.
R. Xu, Y. Guo, X. Han, X. Xia, H. Xiang, and J. Ma, “Opencda: An open cooperative driving automation framework integrated with co-simulation,” 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) , pp. 1155–1162, 2021
2021
Later among the works it cites.
C. Reading, A. Harakeh, J. Chae, and S. L. Waslander, “Categorical depth distribution network for monocular 3d object detection,” CVPR , 2021
2021
Later among the works it cites.
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Cited alongside, same era.
F. Xi, N. Shlezinger, and Y. C. Eldar, “Bilimo: Bit-limited mimo radar via task-based quantization,” IEEE Transactions on Signal Processing , vol. 69, pp. 6267–6282, 2020
2020
Cited alongside, same era.
S. Singh, S. Abu-El-Haija, N. Johnston, J. Ballé, A. Shrivastava, and G. Toderici, “End-to-end learning of compressible features,” in IEEE International Conference on Image Processing , 2020, pp. 3349–3353
2020
Cited alongside, same era.
H. Xie and Z. Qin, “A lite distributed semantic communication system for internet of things,” IEEE Journal on Selected Areas in Communications , vol. 39, pp. 142–153, 2020
2020
Cited alongside, same era.
H. Xie, Z. Qin, G. Y. Li, and B.-H. Juang, “Deep learning enabled semantic communication systems,” IEEE Transactions on Signal Processing , vol. 69, pp. 2663–2675, 2020
2020
Cited alongside, same era.
——, “Bandwidth-agile image transmission with deep joint source-channel coding,” IEEE Transactions on Wireless Communications , vol. 20, pp. 8081–8095, 2020
2020
Cited alongside, same era.
X. Weng, J. Wang, D. Held, and K. Kitani, “3D Multi-Object Tracking: A Baseline and New Evaluation Metrics,” IROS , 2020
2020
Cited alongside, same era.
J. Luiten, A. Osep, P. Dendorfer, P. H. S. Torr, A. Geiger, L. Leal-Taixé, and B. Leibe, “Hota: A higher order metric for evaluating multi-object tracking,” International Journal of Computer Vision , vol. 129, pp. 548 – 578, 2020
2020
Cited alongside, same era.
Y. Li, S. Ren, P. Wu, S. Chen, C. Feng, and W. Zhang, “Learning distilled collaboration graph for multi-agent perception,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
Cited alongside, same era.
Y. Hu, S. Fang, Z. Lei, Y. Zhong, and S. Chen, “Where2comm: Communication-efficient collaborative perception via spatial confidence maps,” Advances in neural information processing systems , vol. 35, pp. 4874–4886, 2022
2022
Later among the works it cites.
R. Xu, H. Xiang, X. Xia, X. Han, J. Liu, and J. Ma, “OPV2V: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication,” ICRA , 2022
2022
Later among the works it cites.
R. Xu, H. Xiang, Z. Tu, X. Xia, M.-H. Yang, and J. Ma, “V2X-ViT: Vehicle-to-everything cooperative perception with vision transformer,” ECCV , 2022
2022
Later among the works it cites.
N. I. Bernardo, J. Zhu, Y. C. Eldar, and J. S. Evans, “Design and analysis of hardware-limited non-uniform task-based quantizers,” IEEE Transactions on Signal Processing , vol. 71, pp. 1551–1562, 2022
2022
Later among the works it cites.
Y. Li, Z. An, Z. Wang, Y. Zhong, S. Chen, and C. Feng, “V2X-Sim: A virtual collaborative perception dataset for autonomous driving,” IEEE Robotics and Automation Letters , vol. 7, 2022
2022
Later among the works it cites.
H. Yu, Y. Luo, M. Shu, Y. Huo, Z. Yang, Y. Shi, Z. Guo, H. Li, X. Hu, J. Yuan et al. , “DAIR-V2X: A large-scale dataset for vehicle-infrastructure cooperative 3d object detection,” In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition (CVPR) , 2022
2022
Later among the works it cites.
R. Xu, Z. Tu, H. Xiang, W. Shao, B. Zhou, and J. Ma, “CoBEVT: Cooperative bird’s eye view semantic segmentation with sparse transformers,” CoRL , 2022
2022
Later among the works it cites.
Z. Lei, S. Ren, Y. Hu, W. Zhang, and S. Chen, “Latency-aware collaborative perception,” ECCV , 2022
2022
Later among the works it cites.
X. Luo, H.-H. Chen, and Q. Guo, “Semantic communications: Overview, open issues, and future research directions,” IEEE Wireless Communications , vol. 29, no. 1, pp. 210–219, 2022
2022
Later among the works it cites.
Y. Hu, S. Fang, W. Xie, and S. Chen, “Aerial monocular 3d object detection,” IEEE Robotics and Automation Letters , 2023
2023
Later among the works it cites.
Y. Hu, Y. Lu, R. Xu, W. Xie, S. Chen, and Y. Wang, “Collaboration helps camera overtake lidar in 3d detection,” 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023
2023
Later among the works it cites.
Y. Lu, Q. Li, B. Liu, M. Dianat, C. Feng, S. Chen, and Y. Wang, “Robust collaborative 3d object detection in presence of pose errors,” IEEE International Conference on Robotics and Automation (ICRA) , 2023
2023
Later among the works it cites.
K. Yang, D. Yang, J. Zhang, M. Li, Y. Liu, J. Liu, H. Wang, P. Sun, and L. Song, “Spatio-temporal domain awareness for multi-agent collaborative perception,” Proceedings of the 31st ACM International Conference on Multimedia , 2023
2023
Later among the works it cites.
K. Yang, D. Yang, J. Zhang, H. Wang, P. Sun, and L. Song, “What2comm: Towards communication-efficient collaborative perception via feature decoupling,” Proceedings of the 31st ACM International Conference on Multimedia , 2023
2023
Later among the works it cites.
T. Wang, G. Chen, K. Chen, Z. Liu, B. Zhang, A. Knoll, and C. Jiang, “Umc: A unified bandwidth-efficient and multi-resolution based collaborative perception framework,” 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023
2023
Later among the works it cites.
D. Yang, K. Yang, Y. Wang, J. Liu, Z. Xu, R. Yin, P. Zhai, and L. Zhang, “How2comm: Communication-efficient and collaboration-pragmatic multi-agent perception,” Advances in Neural Information Processing Systems , 2023
2023
Later among the works it cites.
X. Zhang, H. Zhang, N. Glazer, O. Cohen, E. Reznitskiy, S. Savariego, M. Namer, and Y. C. Eldar, “Hardware implementation of task-based quantization in multiuser signal recovery,” IEEE Transactions on Industrial Electronics , 2023
2023
Later among the works it cites.
R. Xu, X. Xia, J. Li, H. Li, S. Zhang, Z. Tu, Z. Meng, H. Xiang, X. Dong, R. Song, H. Yu, B. Zhou, and J. Ma, “V2v4real: A real-world large-scale dataset for vehicle-to-vehicle cooperative perception,” in The IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR) , 2023
2023
Later among the works it cites.
Y. Hu, Y. Lu, R. Xu, W. Xie, S. Chen, and Y. Wang, “Collaboration helps camera overtake lidar in 3d detection,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023
2023
Later among the works it cites.