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This paper addresses the task of set prediction using deep feed-forward neural networks.
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2018
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2016
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R. Stewart, M. Andriluka, and A. Y. Ng, “End-to-end people detection in crowded scenes,” in CVPR , 2016
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2016
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M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. Salakhutdinov, and A. Smola, “Deep sets,” in NIPS , 2017
2017
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H. Hu, J. Gu, Z. Zhang, J. Dai, and Y. Wei, “Relation networks for object detection,” in CVPR , 2018, pp. 3588–3597
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2018
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2019
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2019
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2019
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L. P. Tchapmi, V. Kosaraju, H. Rezatofighi, I. Reid, and S. Savarese, “TopNet: Structural point cloud decoder,” in CVPR , 2019, pp. 383–392
2019
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A. Sadeghian, V. Kosaraju, A. Sadeghian, N. Hirose, H. Rezatofighi, and S. Savarese, “Sophie: An attentive gan for predicting paths compliant to social and physical constraints,” in CVPR , 2019, pp. 1349–1358
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 CVPR , 2019, pp. 658–666
2019
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2020
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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 European Conference on Computer Vision . Springer, 2020, pp. 213–229
2020
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J. Wang, L. Song, Z. Li, H. Sun, J. Sun, and N. Zheng, “End-to-end object detection with fully convolutional network,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 15 849–15 858
2021
Closest in time.