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  A Closer Look at Self-training for Zero-Label Semantic Segmentation

Pastore, G., Cermelli, F., Xian, Y., Mancini, M., Akata, Z., & Caputo, B. (2021). A Closer Look at Self-training for Zero-Label Semantic Segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 2687-2696). Piscataway, NJ: IEEE. doi:10.1109/CVPRW53098.2021.00303.

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Pastore_A_Closer_Look_at_Self-Training_for_Zero-Label_Semantic_Segmentation_CVPRW_2021_paper.pdf (Preprint), 3MB
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Pastore_A_Closer_Look_at_Self-Training_for_Zero-Label_Semantic_Segmentation_CVPRW_2021_paper.pdf
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These CVPR 2021 workshop 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:
Pastore, Giuseppe1, Author
Cermelli, Fabio1, Author
Xian, Yongqin2, Author           
Mancini, Massimiliano1, Author
Akata, Zeynep2, Author           
Caputo, Barbara1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_1116547              

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Language(s): eng - English
 Dates: 2021
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Pastore_CVPR2021Workshop
DOI: 10.1109/CVPRW53098.2021.00303
 Degree: -

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Title: Learning from Limited and Imperfect Data
Place of Event: Virtual Workshop
Start-/End Date: 2021-06-20 - 2021-06-20

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Project name : DEXIM
Grant ID : 853489
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)
Project name : RoboExNovo
Grant ID : 637076
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
  Abbreviation : CVPR 2021
  Other : CVPRW 2021
  Other : L2ID 2021
Source Genre: Proceedings
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Affiliations:
Publ. Info: Piscataway, NJ : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 2687 - 2696 Identifier: ISBN: 978-1-6654-4899-4