References

[Ber92]

Dimitri P Bertsekas. Auction algorithms for network flow problems: a tutorial introduction. Computational optimization and applications, 1:7–66, 1992.

[CMS+20]

Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, 213–229. Springer, 2020.

[Cut13]

Marco Cuturi. Sinkhorn distances: lightspeed computation of optimal transport. Advances in neural information processing systems, 2013.

[DPDPS+23]

Henri De Plaen, Pierre-François De Plaen, Johan A. K. Suykens, Marc Proesmans, Tinne Tuytelaars, and Luc Van Gool. Unbalanced optimal transport: a unified framework for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 3198–3207. June 2023.

[Kan58]

L. Kantorovitch. On the translocation of masses. Management Science, 5(1):1–4, 1958. URL: http://www.jstor.org/stable/2626967 (visited on 2022-11-03).

[Kuh55]

Harold W Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2):83–97, 1955.

[LZL+22]

Feng Li, Hao Zhang, Shilong Liu, Jian Guo, Lionel M Ni, and Lei Zhang. Dn-detr: accelerate detr training by introducing query denoising. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13619–13627. 2022.

[Mon81]

Gaspard Monge. Mémoire sur la théorie des déblais et des remblais. Mem. Math. Phys. Acad. Royale Sci., pages 666–704, 1781.

[Mun57]

James Munkres. Algorithms for the assignment and transportation problems. Journal of the society for industrial and applied mathematics, 5(1):32–38, 1957.

[PeyreC+19]

Gabriel Peyré, Marco Cuturi, and others. Computational optimal transport: with applications to data science. Foundations and Trends® in Machine Learning, 11(5-6):355–607, 2019.

[Vil09]

Cédric Villani. Optimal transport: old and new. Volume 338. Springer, 2009.

[ZSL+21]

Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable detr: deformable transformers for end-to-end object detection. In International Conference on Learning Representations. 2021. URL: https://openreview.net/forum?id=gZ9hCDWe6ke.