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Previous knowledge distillation (KD) methods for object detection mostly focus on feature imitation instead of mimicking the prediction logits due to its inefficiency in distilling the localization information.
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2015
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2017
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2017
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Q. Li, S. Jin, and J. Yan, “Mimicking very efficient network for object detection,” in
2017
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2017
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2017
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2017
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2017
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2017
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2018
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2018
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2018
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2018
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2018
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B. Jiang, R. Luo, J. Mao, T. Xiao, and Y. Jiang, “Acquisition of localization confidence for accurate object detection,” in
2018
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J. Ma, W. Shao, H. Ye, L. Wang, H. Wang, Y. Zheng, and X. Xue, “Arbitrary-oriented scene text detection via rotation proposals,”
2018
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G.-S. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, and L. Zhang, “Dota: A large-scale dataset for object detection in aerial images,” in
2018
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H. Li, Z. Xu, G. Taylor, C. Studer, and T. Goldstein, “Visualizing the loss landscape of neural nets,”
2018
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T. Furlanello, Z. Lipton, M. Tschannen, L. Itti, and A. Anandkumar, “Born again neural networks,” in
2018
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X. Jin, B. Peng, Y. Wu, Y. Liu, J. Liu, D. Liang, J. Yan, and X. Hu, “Knowledge distillation via route constrained optimization,” in
2019
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T. Wang, L. Yuan, X. Zhang, and J. Feng, “Distilling object detectors with fine-grained feature imitation,” in
2019
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Z. Tian, C. Shen, H. Chen, and T. He, “FCOS: Fully convolutional one-stage object detection,” in
2019
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W. Park, D. Kim, Y. Lu, and M. Cho, “Relational knowledge distillation,” in
2019
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J. Wang, K. Chen, S. Yang, C. C. Loy, and D. Lin, “Region proposal by guided anchoring,” in
2019
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C. Zhu, Y. He, and M. Savvides, “Feature selective anchor-free module for single-shot object detection,” in
2019
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X. Lu, B. Li, Y. Yue, Q. Li, and J. Yan, “Grid R-CNN,” in
2019
S. Zhang, C. Chi, Y. Yao, Z. Lei, and S. Z. Li, “Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection,” in
2020
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C. Zhu, F. Chen, Z. Shen, and M. Savvides, “Soft anchor-point object detection,” in
2020
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H. Qiu, Y. Ma, Z. Li, S. Liu, and J. Sun, “Borderdet: Border feature for dense object detection,” in
2020
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2020
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K. Kim and H. S. Lee, “Probabilistic anchor assignment with iou prediction for object detection,” in
2020
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Cited alongside, same era.
X. Yang, J. Yang, J. Yan, Y. Zhang, T. Zhang, Z. Guo, X. Sun, and K. Fu, “Scrdet: Towards more robust detection for small, cluttered and rotated objects,” in
2019
Cited alongside, same era.
L. Han, P. Tao, and R. R. Martin, “Livestock detection in aerial images using a fully convolutional network,”
2019
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J. Pang, K. Chen, J. Shi, H. Feng, W. Ouyang, and D. Lin, “Libra R-CNN: Towards balanced learning for object detection,” in
2019
Cited alongside, same era.
H. Rezatofighi, N. Tsoi, J. Gwak, A. Sadeghian, I. Reid, and S. Savarese, “Generalized Intersection over Union: A metric and a loss for bounding box regression,” in
2019
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Y. He, C. Zhu, J. Wang, M. Savvides, and X. Zhang, “Bounding box regression with uncertainty for accurate object detection,” in
2019
Cited alongside, same era.
J. Choi, D. Chun, H. Kim, and H.-J. Lee, “Gaussian YOLOv3: An accurate and fast object detector using localization uncertainty for autonomous driving,” in
2019
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N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in
2020
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G.-H. Wang, Y. Ge, and J. Wu, “Distilling knowledge by mimicking features,”
2021
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Z. Kang, P. Zhang, X. Zhang, J. Sun, and N. Zheng, “Instance-conditional knowledge distillation for object detection,” in
2021
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W. Son, J. Na, J. Choi, and W. Hwang, “Densely guided knowledge distillation using multiple teacher assistants,” in
2021
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X. Dai, Z. Jiang, Z. Wu, Y. Bao, Z. Wang, S. Liu, and E. Zhou, “General instance distillation for object detection,” in
2021
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J. Guo, K. Han, Y. Wang, H. Wu, X. Chen, C. Xu, and C. Xu, “Distilling object detectors via decoupled features,” in
2021
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D. Zhixing, R. Zhang, M. Chang, S. Liu, T. Chen, Y. Chen
2021
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X. Yang, J. Yan, Q. Ming, W. Wang, X. Zhang, and Q. Tian, “Rethinking rotated object detection with gaussian wasserstein distance loss,” in
2021
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X. Yang, X. Yang, J. Yang, Q. Ming, W. Wang, Q. Tian, and J. Yan, “Learning high-precision bounding box for rotated object detection via kullback-leibler divergence,” in
2021
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H. Zhang, Y. Wang, F. Dayoub, and N. Sünderhauf, “Varifocalnet: An iou-aware dense object detector,” in
2021
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Z. Zheng, P. Wang, D. Ren, W. Liu, R. Ye, Q. Hu, and W. Zuo, “Enhancing geometric factors in model learning and inference for object detection and instance segmentation,”
2021
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X. Li, W. Wang, X. Hu, J. Li, J. Tang, and J. Yang, “Generalized focal loss v2: Learning reliable localization quality estimation for dense object detection,” in
2021
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W. Qian, X. Yang, S. Peng, Y. Guo, and J. Yan, “Learning modulated loss for rotated object detection,” in
2021
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X. Yang, Q. Liu, J. Yan, A. Li, Z. Zhang, and G. Yu, “R3det: Refined single-stage detector with feature refinement for rotating object,” in
2021
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S.-H. Gao, M.-M. Cheng, K. Zhao, X.-Y. Zhang, M.-H. Yang, and P. Torr, “Res2net: A new multi-scale backbone architecture,”
2021
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Z. Ge, S. Liu, Z. Li, O. Yoshie, and J. Sun, “OTA: Optimal transport assignment for object detection,” in
2021
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Z. Dai, H. Liu, Q. Le, and M. Tan, “Coatnet: Marrying convolution and attention for all data sizes,”
2021
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M.-H. Guo, J.-X. Cai, Z.-N. Liu, T.-J. Mu, R. R. Martin, and S.-M. Hu, “Pct: Point cloud transformer,”
2021
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Z. Zheng, R. Ye, P. Wang, D. Ren, W. Zuo, Q. Hou, and M. Cheng, “Localization distillation for dense object detection,” in
2022
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G. Li, X. Li, Y. Wang, S. Zhang, Y. Wu, and D. Liang, “Knowledge distillation for object detection via rank mimicking and prediction-guided feature imitation,” in
2022
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Y. Zhou, X. Yang, G. Zhang, J. Wang, Y. Liu, L. Hou, X. Jiang, X. Liu, J. Yan, C. Lyu, W. Zhang, and K. Chen, “Mmrotate: A rotated object detection benchmark using pytorch,” in
2022
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B. Zhao, Q. Cui, R. Song, Y. Qiu, and J. Liang, “Decoupled knowledge distillation,” in
2022
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Y.-H. Wu, Y. Liu, X. Zhan, and M.-M. Cheng, “P2T: Pyramid pooling transformer for scene understanding,”
2022
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Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A convnet for the 2020s,” in
2022
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