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The recently proposed DEtection TRansformer (DETR) has established a fully end-to-end paradigm for object detection.
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2019
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 ICLR , 2021
2021
Later among the works it cites.
D. Meng, X. Chen, Z. Fan, G. Zeng, H. Li, Y. Yuan, L. Sun, and J. Wang, “Conditional DETR for fast training convergence,” in ICCV , 2021
2021
Later among the works it cites.
P. Gao, M. Zheng, X. Wang, J. Dai, and H. Li, “Fast convergence of DETR with spatially modulated co-attention,” in ICCV , 2021
2021
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N. Wang, W. Zhou, J. Wang, and H. Li, “Transformer meets tracker: Exploiting temporal context for robust visual tracking,” in CVPR , 2021
2021
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2021
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M. Zheng, S. Karanam, Z. Wu, and R. J. Radke, “Re-identification with consistent attentive siamese networks,” in CVPR , 2019
2019
Cited alongside, same era.
T.-I. Hsieh, Y.-C. Lo, H.-T. Chen, and T.-L. Liu, “One-shot object detection with co-attention and co-excitation,” in NeurIPS , 2019
2019
Cited alongside, same era.
J. Pang, K. Chen, J. Shi, H. Feng, W. Ouyang, and D. Lin, “Libra R-CNN: Towards balanced learning for object detection,” in CVPR , 2019
2019
Cited alongside, same era.
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2019
Cited alongside, same era.
X. Yan, Z. Chen, A. Xu, X. Wang, X. Liang, and L. Lin, “Meta R-CNN: Towards general solver for instance-level low-shot learning,” in ICCV , 2019
2019
Cited alongside, same era.
B. Kang, Z. Liu, X. Wang, F. Yu, J. Feng, and T. Darrell, “Few-shot object detection via feature reweighting,” in ICCV , 2019
2019
Cited alongside, same era.
Q. Zhao, T. Sheng, Y. Wang, Z. Tang, Y. Chen, L. Cai, and H. Ling, “M2Det: A single-shot object detector based on multi-level feature pyramid network,” in AAAI , 2019
2019
Cited alongside, same era.
2021
Later among the works it cites.
Z. Cai and N. Vasconcelos, “Cascade R-CNN: High quality object detection and instance segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43, pp. 1483–1498, 2021
2021
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M. Liao, P. Lyu, M. He, C. Yao, W. Wu, and X. Bai, “Mask TextSpotter: An end-to-end trainable neural network for spotting text with arbitrary shapes,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43, no. 2, pp. 532–548, 2021
2021
Later among the works it cites.
G. Zhang, K. Cui, R. Wu, S. Lu, and Y. Tian, “PNPDet: Efficient few-shot detection without forgetting via plug-and-play sub-networks,” in WACV , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
T. Wang, L. Yuan, Y. Chen, J. Feng, and S. Yan, “PnP-DETR: Towards efficient visual analysis with Transformers,” in ICCV , 2021
2021
Later among the works it cites.
S. He, H. Luo, P. Wang, F. Wang, H. Li, and W. Jiang, “TransReID: Transformer-based object re-identification,” in ICCV , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
X. Dai, Y. Chen, J. Yang, P. Zhang, L. Yuan, and L. Zhang, “Dynamic DETR: End-to-end object detection with dynamic attention,” in ICCV , 2021
2021
Later among the works it cites.
G. Zhang, K. Cui, T.-Y. Hung, and S. Lu, “Defect-GAN: High-fidelity defect synthesis for automated defect inspection,” in WACV , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
P. Sun, R. Zhang, Y. Jiang, T. Kong, C. Xu, W. Zhan, M. Tomizuka, L. Li, Z. Yuan, C. Wang, and P. Luo, “Sparse R-CNN: End-to-end object detection with learnable proposals,” in CVPR , 2021
2021
Later among the works it cites.
Z. Sun, S. Cao, Y. Yang, and K. M. Kitani, “Rethinking Transformer-based set prediction for object detection,” in ICCV , 2021
2021
Later among the works it cites.
Z. Dai, B. Cai, Y. Lin, and J. Chen, “UP-DETR: Unsupervised pre-training for object detection with transformers,” in CVPR , 2021
2021
Later among the works it cites.
Y. Wang, X. Zhang, T. Yang, and J. Sun, “Anchor DETR: Query design for Transformer-based detector,” in AAAI , 2022
2022
Closest in time.
G. Zhang, Z. Luo, Y. Yu, K. Cui, and S. Lu, “Accelerating DETR convergence via semantic-aligned matching,” in CVPR , 2022
2022
Closest in time.
F. Li, H. Zhang, S. Liu, J. Guo, L. M. Ni, and L. Zhang, “DN-DETR: Accelerate DETR training by introducing query denoising,” in CVPR , 2022
2022
Closest in time.
B. Roh, J. Shin, W. Shin, and S. Kim, “Sparse DETR: Efficient end-to-end object detection with learnable sparsity,” in ICLR , 2022
2022
Closest in time.
H. Song, D. Sun, S. Chun, V. Jampani, D. Han, B. Heo, W. Kim, and M.-H. Yang, “ViDT: An efficient and effective fully transformer-based object detector,” in ICLR , 2022
2022
Closest in time.
S. Liu, F. Li, H. Zhang, X. Yang, X. Qi, H. Su, J. Zhu, and L. Zhang, “DAB-DETR: Dynamic anchor boxes are better queries for DETR,” in ICLR , 2022
2022
Closest in time.
X. Cao, P. Yuan, B. Feng, and K. Niu, “CF-DETR: Coarse-to-fine transformers for end-to-end object detection,” in AAAI , 2022
2022
Closest in time.