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  Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths

Horňáková, A., Kaiser, T., Swoboda, P., Rolinek, M., Rosenhahn, B., & Henschel, R. (2021). Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths. In ICCV 2021 (pp. 6310-6320). Piscataway, NJ: IEEE. doi:10.1109/ICCV48922.2021.00627.

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Genre: Conference Paper
Latex : Making Higher Order {MOT} Scalable: {A}n Efficient Approximate Solver for Lifted Disjoint Paths

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Hornakova_Making_Higher_Order_MOT_Scalable_An_Efficient_Approximate_Solver_for_ICCV_2021_paper.pdf (Preprint), 624KB
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Hornakova_Making_Higher_Order_MOT_Scalable_An_Efficient_Approximate_Solver_for_ICCV_2021_paper.pdf
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These ICCV 2021 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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 Creators:
Horňáková, Andrea1, Author           
Kaiser, Timo2, Author
Swoboda, Paul1, Author           
Rolinek, Michal2, Author
Rosenhahn, Bodo2, Author
Henschel, Roberto2, Author
Affiliations:
1Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_1116547              
2External Organizations, ou_persistent22              

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Language(s): eng - English
 Dates: 2021-06-142021-08-1220212021
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: HornakovaICCV2021
DOI: 10.1109/ICCV48922.2021.00627
 Degree: -

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Title: IEEE/CVF International Conference on Computer Vision
Place of Event: Virtual Event
Start-/End Date: 2021-10-11 - 2021-10-17

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Project name : DEXIM
Grant ID : 853489
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: ICCV 2021
  Abbreviation : ICCV 2021
  Subtitle : 2021 IEEE/CVF International Conference on Computer Vision ; Proceedings
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 6310 - 6320 Identifier: ISBN: 978-1-6654-2812-5