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Recent one-stage object detectors follow a per-pixel prediction approach that predicts both the object category scores and boundary positions from every single grid location.
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J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 779–788
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
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T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2980–2988
2017
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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 conference on computer vision and pattern recognition , 2017, pp. 2117–2125
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K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2961–2969
2017
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J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 764–773
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Z. Cai and N. Vasconcelos, “Cascade r-cnn: Delving into high quality object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 6154–6162
2018
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B. Singh and L. S. Davis, “An analysis of scale invariance in object detection snip,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 3578–3587
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 Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 784–799
2018
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S. Zhang, L. Wen, X. Bian, Z. Lei, and S. Z. Li, “Single-shot refinement neural network for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4203–4212
2018
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2019
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K. Duan, S. Bai, L. Xie, H. Qi, Q. Huang, and Q. Tian, “Centernet: Keypoint triplets for object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 6569–6578
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2019
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X. Zhou, J. Zhuo, and P. Krahenbuhl, “Bottom-up object detection by grouping extreme and center points,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 850–859
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H. Law and J. Deng, “Cornernet: Detecting objects as paired keypoints,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 734–750
2018
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Z. Tian, C. Shen, H. Chen, and T. He, “Fcos: Fully convolutional one-stage object detection,” in Proceedings of the IEEE international conference on computer vision , 2019, pp. 9627–9636
2019
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C. Zhu, Y. He, and M. Savvides, “Feature selective anchor-free module for single-shot object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 840–849
2019
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Y. Li, Y. Chen, N. Wang, and Z. Zhang, “Scale-aware trident networks for object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 6054–6063
2019
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X. Zhu, H. Hu, S. Lin, and J. Dai, “Deformable convnets v2: More deformable, better results,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 9308–9316
2019
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J. Wang, K. Chen, S. Yang, C. C. Loy, and D. Lin, “Region proposal by guided anchoring,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 2965–2974
2019
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T. Vu, H. Jang, T. X. Pham, and C. Yoo, “Cascade rpn: Delving into high-quality region proposal network with adaptive convolution,” in Advances in Neural Information Processing Systems , 2019, pp. 1432–1442
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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 821–830
2019
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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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 658–666
2019
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T. Kong, F. Sun, H. Liu, Y. Jiang, L. Li, and J. Shi, “Foveabox: Beyound anchor-based object detection,” IEEE Transactions on Image Processing , vol. 29, pp. 7389–7398, 2020
2020
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C. Zhu, F. Chen, Z. Shen, and M. Savvides, “Soft anchor-point object detection,” in European conference on computer vision . Springer, 2020, pp. 91–107
2020
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H. Zhang, H. Chang, B. Ma, N. Wang, and X. Chen, “Dynamic r-cnn: Towards high quality object detection via dynamic training,” in European Conference on Computer Vision . Springer, 2020, pp. 260–275
2020
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W. Ke, T. Zhang, Z. Huang, Q. Ye, J. Liu, and D. Huang, “Multiple anchor learning for visual object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 10 206–10 215
2020
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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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9759–9768
2020
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X. Li, W. Wang, L. Wu, S. Chen, X. Hu, J. Li, J. Tang, and J. Yang, “Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection,” in NeurIPS , 2020
2020
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Y. Wu, Y. Chen, L. Yuan, Z. Liu, L. Wang, H. Li, and Y. Fu, “Rethinking classification and localization for object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 10 186–10 195
2020
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G. Song, Y. Liu, and X. Wang, “Revisiting the sibling head in object detector,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 563–11 572
2020
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J. Wang, W. Zhang, Y. Cao, K. Chen, J. Pang, T. Gong, J. Shi, C. C. Loy, and D. Lin, “Side-aware boundary localization for more precise object detection,” in European Conference on Computer Vision . Springer, 2020, pp. 403–419
2020
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Y. Chen, Z. Zhang, Y. Cao, L. Wang, S. Lin, and H. Hu, “Reppoints v2: Verification meets regression for object detection,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
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H. Qiu, Y. Ma, Z. Li, S. Liu, and J. Sun, “Borderdet: Border feature for dense object detection,” in European Conference on Computer Vision . Springer, 2020, pp. 549–564
2020
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