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The Detection Transformer (DETR) has revolutionized the design of CNN-based object detection systems, showcasing impressive performance.
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, pp. 91–99, 2015
2015
Earlier work this paper cites.
N. Ballas, L. Yao, C. Pal, and A. Courville, “Delving deeper into convolutional networks for learning video representations,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2015, pp. 1–9
2015
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in International Conference on Learning Representations , 2016, pp. 1–14
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Earlier work this paper cites.
N. Bodla, B. Singh, R. Chellappa, and L. S. Davis, “Soft-nms–improving object detection with one line of code,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2017, pp. 5561–5569
2017
Earlier work this paper cites.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” in International Conference on Learning Representations , 2017, pp. 1–12
2017
Earlier work this paper cites.
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/CVF Conference on Computer Vision and Pattern Recognition , 2017, pp. 2117–2125
2017
Earlier work this paper cites.
L. N. Smith, “Cyclical learning rates for training neural networks,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision . IEEE, 2017, pp. 464–472
2017
Earlier work this paper cites.
Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 4490–4499
2018
Earlier work this paper cites.
Y. Yan, Y. Mao, and B. Li, “Second: Sparsely embedded convolutional detection,” Sensors , vol. 18, no. 10, p. 3337, 2018
2018
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Annual Conference of the North American Chapter of the Association for Computational Linguistics , 2019, pp. 4171–4186
2019
Earlier work this paper cites.
J. Lu, D. Batra, D. Parikh, and S. Lee, “Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Earlier work this paper cites.
S. Shi, X. Wang, and H. Li, “Pointrcnn: 3d object proposal generation and detection from point cloud,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 770–779
2019
Earlier work this paper cites.
H. Xu, C. Jiang, X. Liang, and Z. Li, “Spatial-aware graph relation network for large-scale object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 9298–9307
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
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
Earlier work this paper cites.
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, V. Vasudevan, W. Han, J. Ngiam, H. Zhao, A. Timofeev, S. Ettinger, M. Krivokon, A. Gao, A. Joshi, Y. Zhang, J. Shlens, Z. Chen, and D. Anguelov, “Scalability in perception for autonomous driving: Waymo open dataset,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 2443–2451
2020
Earlier work this paper cites.
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
Earlier work this paper cites.
R. Huang, W. Zhang, A. Kundu, C. Pantofaru, D. A. Ross, T. Funkhouser, and A. Fathi, “An lstm approach to temporal 3d object detection in lidar point clouds,” in European Conference on Computer Vision , 2020, pp. 266–282
2020
Earlier work this paper cites.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European Conference on Computer Vision , 2020, pp. 213–229
2020
Earlier work this paper cites.
Z. Yang, Y. Sun, S. Liu, and J. Jia, “3dssd: Point-based 3d single stage object detector,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 040–11 048
2020
Earlier work this paper cites.
C. Yu, X. Ma, J. Ren, H. Zhao, and S. Yi, “Spatio-temporal graph transformer networks for pedestrian trajectory prediction,” in European Conference on Computer Vision , 2020, pp. 507–523
2020
Earlier work this paper cites.
C. He, H. Zeng, J. Huang, X.-S. Hua, and L. Zhang, “Structure aware single-stage 3d object detection from point cloud,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 873–11 882
2020
Cited alongside, same era.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” IEEE transactions on neural networks and learning systems , vol. 32, no. 1, pp. 4–24, 2020
2020
Cited alongside, same era.
Y. Wang, S. Zhao, R. Zhang, X. Cheng, and L. Yang, “Multi-vehicle collaborative learning for trajectory prediction with spatio-temporal tensor fusion,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 1, pp. 236–248, 2020
2020
Cited alongside, same era.
W. Shi and R. Rajkumar, “Point-gnn: Graph neural network for 3d object detection in a point cloud,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 1711–1719
2020
Z. Zhou, X. Zhao, Y. Wang, P. Wang, and H. Foroosh, “Centerformer: Center-based transformer for 3d object detection,” in European Conference on Computer Vision , 2022, pp. 496–513
2022
Later among the works it cites.
Q. He, Z. Wang, H. Zeng, Y. Zeng, and Y. Liu, “Svga-net: Sparse voxel-graph attention network for 3d object detection from point clouds,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2022, pp. 870–878
2022
Later among the works it cites.
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He, and D. Cai, “Graph r-cnn: Towards accurate 3d object detection with semantic-decorated local graph,” in European Conference on Computer Vision . Springer, 2022, pp. 662–679
2022
Later among the works it cites.
Y. Zeng, D. Zhang, C. Wang, Z. Miao, T. Liu, X. Zhan, D. Hao, and C. Ma, “Lift: Learning 4d lidar image fusion transformer for 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 17 172–17 181
2022
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Cited alongside, same era.
S. Shi, Z. Wang, J. Shi, X. Wang, and H. Li, “From points to parts: 3d object detection from point cloud with part-aware and part-aggregation network,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43, no. 8, pp. 2647–2664, 2020
2020
Cited alongside, same era.
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
2021
Cited alongside, same era.
Z. Yang, Y. Zhou, Z. Chen, and J. Ngiam, “3d-man: 3d multi-frame attention network for object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 1863–1872
2021
Cited alongside, same era.
J. Yin, J. Shen, X. Gao, D. Crandall, and R. Yang, “Graph neural network and spatiotemporal transformer attention for 3d video object detection from point clouds,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021
2021
Cited alongside, same era.
C. R. Qi, Y. Zhou, M. Najibi, P. Sun, K. Vo, B. Deng, and D. Anguelov, “Offboard 3d object detection from point cloud sequences,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 6134–6144
2021
Cited alongside, same era.
A. Prakash, K. Chitta, and A. Geiger, “Multi-modal fusion transformer for end-to-end autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 7077–7087
2021
Cited alongside, same era.
X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai, “Deformable detr: Deformable transformers for end-to-end object detection,” in International Conference on Learning Representations , 2021
2021
Cited alongside, same era.
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang, and H. Li, “Voxel r-cnn: Towards high performance voxel-based 3d object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2021, pp. 1201–1209
2021
Cited alongside, same era.
Later among the works it cites.
P. Sun, M. Tan, W. Wang, C. Liu, F. Xia, Z. Leng, and D. Anguelov, “Swformer: Sparse window transformer for 3d object detection in point clouds,” in European Conference on Computer Vision , 2022, pp. 426–442
2022
Later among the works it cites.
D.-K. Nguyen, J. Ju, O. Booij, M. R. Oswald, and C. G. Snoek, “Boxer: Box-attention for 2d and 3d transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 4773–4782
2022
Later among the works it cites.
2022
Later among the works it cites.
C. He, R. Li, S. Li, and L. Zhang, “Voxel set transformer: A set-to-set approach to 3d object detection from point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 8417–8427
2022
Later among the works it cites.
G. Shi, R. Li, and C. Ma, “Pillarnet: Real-time and high-performance pillar-based 3d object detection,” in European Conference on Computer Vision , 2022, pp. 35–52
2022
Later among the works it cites.
Y. Chen, Y. Li, X. Zhang, J. Sun, and J. Jia, “Focal sparse convolutional networks for 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 5428–5437
2022
Later among the works it cites.
X. Bai, Z. Hu, X. Zhu, Q. Huang, Y. Chen, H. Fu, and C.-L. Tai, “Transfusion: Robust lidar-camera fusion for 3d object detection with transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 1090–1099
2022
Later among the works it cites.
F. Li, H. Zhang, S. Liu, J. Guo, L. M. Ni, and L. Zhang, “Dn-detr: Accelerate detr training by introducing query denoising,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 13 619–13 627
2022
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2022
Later among the works it cites.
Y. Chen, Y. Li, X. Zhang, J. Sun, and J. Jia, “Focal sparse convolutional networks for 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 5428–5437
2022
Later among the works it cites.
X. Bai, Z. Hu, X. Zhu, Q. Huang, Y. Chen, H. Fu, and C.-L. Tai, “Transfusion: Robust lidar-camera fusion for 3d object detection with transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 1090–1099
2022
Later among the works it cites.
Y. Zhang, Q. Zhang, Z. Zhu, J. Hou, and Y. Yuan, “Glenet: Boosting 3d object detectors with generative label uncertainty estimation,” International Journal of Computer Vision , pp. 3332–3352, 2023
2023
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Y. Zhang, Q. Zhang, J. Hou, Y. Yuan, and G. Xing, “Unleash the potential of image branch for cross-modal 3d object detection,” in Advances in Neural Information Processing Systems , 2023
2023
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C. He, R. Li, Y. Zhang, S. Li, and L. Zhang, “Msf: Motion-guided sequential fusion for efficient 3d object detection from point cloud sequences,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5196–5205
2023
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H. Zhang, F. Li, S. Liu, L. Zhang, H. Su, J. Zhu, L. Ni, and H.-Y. Shum, “DINO: DETR with improved denoising anchor boxes for end-to-end object detection,” in International Conference on Learning Representations , 2023
2023
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S. Shi, L. Jiang, J. Deng, Z. Wang, C. Guo, J. Shi, X. Wang, and H. Li, “Pv-rcnn++: Point-voxel feature set abstraction with local vector representation for 3d object detection,” International Journal of Computer Vision , vol. 131, no. 2, pp. 531–551, 2023
2023
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Z. Luo, G. Zhang, C. Zhou, T. Liu, S. Lu, and L. Pan, “Transpillars: Coarse-to-fine aggregation for multi-frame 3d object detection,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2023, pp. 4230–4239
2023
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F. Chen, H. Zhang, K. Hu, Y.-K. Huang, C. Zhu, and M. Savvides, “Enhanced training of query-based object detection via selective query recollection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 23 756–23 765
2023
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H. Zhang, F. Li, S. Liu, L. Zhang, H. Su, J. Zhu, L. Ni, and H.-Y. Shum, “DINO: DETR with improved denoising anchor boxes for end-to-end object detection,” in International Conference on Learning Representations , 2023
2023
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Z. Luo, G. Zhang, C. Zhou, T. Liu, S. Lu, and L. Pan, “Transpillars: Coarse-to-fine aggregation for multi-frame 3d object detection,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2023, pp. 4230–4239
2023
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