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  Compositional Mixture Representations for Vision and Text

Alaniz, S., Federici, M., & Akata, Z. (2022). Compositional Mixture Representations for Vision and Text. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 4201-4210). Piscataway, NJ: IEEE. doi:10.1109/CVPRW56347.2022.00465.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000B-F2B1-B Version Permalink: https://hdl.handle.net/21.11116/0000-000B-F2B2-A
Genre: Conference Paper

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 Creators:
Alaniz, Stephan1, Author
Federici, Marco1, Author
Akata, Zeynep2, 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: 2022
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Alaniz_CVPRW2022
DOI: 10.1109/CVPRW56347.2022.00465
 Degree: -

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Title: Workshop on Learning with Limited Labelled Data for Image and Video Understanding
Place of Event: New Orleans, LA, USA
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)

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Title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
  Other : CVPRW 2022
  Abbreviation : CVPR 2022
  Other : L3D-IVU - CVPR 2022
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Piscataway, NJ : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 4201 - 4210 Identifier: ISBN: 978-1-6654-8739-9