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  Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing

Agarwal, V., Shetty, R., & Fritz, M. (2020). Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9687-9695). Piscataway, NJ: IEEE. doi:10.1109/CVPR42600.2020.00971.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0005-7482-5 Version Permalink: https://hdl.handle.net/21.11116/0000-0007-AE05-0
Genre: Conference Paper
Latex : Towards Causal {VQA}: {R}evealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing

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arXiv:1912.07538.pdf (Preprint), 9MB
 
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 Creators:
Agarwal, Vedika1, Author           
Shetty, Rakshith1, Author           
Fritz, Mario2, 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: 2019-12-162019-12-222020
 Publication Status: Published online
 Pages: 15
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: agarwal2020towards
DOI: 10.1109/CVPR42600.2020.00971
 Degree: -

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Title: 33rd IEEE Conference on Computer Vision and Pattern Recognition
Place of Event: Seattle, WA, USA (Virtual)
Start-/End Date: 2020-06-14 - 2020-06-19

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Title: IEEE/CVF Conference on Computer Vision and Pattern Recognition
  Abbreviation : CVPR 2020
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 9687 - 9695 Identifier: ISBN: 978-1-7281-7168-5