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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 IEEE/CVF International Conference on Computer Vision (pp. 6310-6320). Piscataway, NJ: IEEE. doi:10.1109/ICCV48922.2021.00627.

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Genre: Konferenzbeitrag
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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Urheber

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 Urheber:
Horňáková, Andrea1, Autor           
Kaiser, Timo2, Autor
Swoboda, Paul1, Autor           
Rolinek, Michal2, Autor
Rosenhahn, Bodo2, Autor
Henschel, Roberto2, Autor
Affiliations:
1Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_1116547              
2External Organizations, ou_persistent22              

Inhalt

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Sprache(n): eng - English
 Datum: 2021-06-142021-08-1220212021
 Publikationsstatus: Online veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: HornakovaICCV2021
DOI: 10.1109/ICCV48922.2021.00627
 Art des Abschluß: -

Veranstaltung

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Titel: International Conference on Computer Vision
Veranstaltungsort: Virtual Event
Start-/Enddatum: 2021-10-11 - 2021-10-17

Entscheidung

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Projektinformation

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Projektname : DEXIM
Grant ID : 853489
Förderprogramm : Horizon 2020 (H2020)
Förderorganisation : European Commission (EC)

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Titel: IEEE/CVF International Conference on Computer Vision
  Kurztitel : ICCV 2021
Genre der Quelle: Konferenzband
 Urheber:
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Ort, Verlag, Ausgabe: Piscataway, NJ : IEEE
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 6310 - 6320 Identifikator: ISBN: 978-1-6654-2812-5