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In this paper, we propose an algorithm, named hashing-based non-maximum suppression (HNMS) to efficiently suppress the non-maximum boxes for object detection.
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Liu, S., Lu, C., Jia, J.: Box aggregation for proposal decimation: Last mile of object detection. In: 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 7-13, 2015. pp. 2569–2577 (2015)
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Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C., Berg, A.C.: SSD: single shot multibox detector. In: Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part I. pp. 21–37 (2016)
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Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39
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Bodla, N., Singh, B., Chellappa, R., Davis, L.S.: Soft-nms - improving object detection with one line of code. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017. pp. 5562–5570 (2017). https://doi.org/10.1109/ICCV.2017.593, https://doi.org/10.1109/ICCV.2017.593
Lin, T., Goyal, P., Girshick, R.B., He, K., Dollár, P.: Focal loss for dense object detection. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017. pp. 2999–3007. IEEE Computer Society (2017). https://doi.org/10.1109/ICCV.2017.324, https://doi.org/10.1109/ICCV.2017.324
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Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017. pp. 6517–6525 (2017). https://doi.org/10.1109/CVPR.2017.690, https://doi.org/10.1109/CVPR.2017.690
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Wang, J., Zhang, T., Song, J., Sebe, N., Shen, H.T.: A survey on learning to hash. IEEE Trans. Pattern Anal. Mach. Intell. 40
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2018
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2017
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Hosang, J.H., Benenson, R., Schiele, B.: Learning non-maximum suppression. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017. pp. 6469–6477 (2017). https://doi.org/10.1109/CVPR.2017.685, https://doi.org/10.1109/CVPR.2017.685
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Hsieh, M., Lin, Y., Hsu, W.H.: Drone-based object counting by spatially regularized regional proposal network. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017. pp. 4165–4173 (2017). https://doi.org/10.1109/ICCV.2017.446, http://doi.ieeecomputersociety.org/10.1109/ICCV.2017.446
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Lin, T., Dollár, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017. pp. 936–944 (2017). https://doi.org/10.1109/CVPR.2017.106, https://doi.org/10.1109/CVPR.2017.106
2017
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Later among the works it cites.
Cai, L., Zhao, B., Wang, Z., Lin, J., Foo, C.S., Aly, M.M.S., Chandrasekhar, V.: Maxpoolnms: Getting rid of NMS bottlenecks in two-stage object detectors. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019. pp. 9356–9364 (2019)
2019
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Goldman, E., Herzig, R., Eisenschtat, A., Goldberger, J., Hassner, T.: Precise detection in densely packed scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019. pp. 5227–5236 (2019)
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He, Y., Zhu, C., Wang, J., Savvides, M., Zhang, X.: Bounding box regression with uncertainty for accurate object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019. pp. 2888–2897 (2019)
2019
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