Conference item
Discovering relationships between object categories via universal canonical maps
- Abstract:
-
We tackle the problem of learning the geometry of multiple categories of deformable objects jointly. Recent work has shown that it is possible to learn a unified dense pose predictor for several categories of related objects. However, training such models requires to initialize inter-category correspondences by hand. This is suboptimal and the resulting models fail to maintain correct correspondences as individual categories are learned. In this paper, we show that improved correspondences ca...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Bibliographic Details
- Publisher:
- IEEE Publisher's website
- Host title:
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Pages:
- 404-413
- Publication date:
- 2021-11-13
- Acceptance date:
- 2021-06-01
- Event title:
- Conference on Computer Vision and Pattern Recognition (CVPR 2021)
- Event location:
- Virtual event
- Event website:
- https://cvpr2021.thecvf.com/
- Event start date:
- 2021-06-19
- Event end date:
- 2021-06-25
- DOI:
- EISSN:
-
2575-7075
- ISSN:
-
1063-6919
- EISBN:
- 9781665445092
- ISBN:
- 9781665445108
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1237038
- Local pid:
- pubs:1237038
- Deposit date:
- 2022-02-28
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2021
- Rights statement:
- © 2021 IEEE.
- Notes:
- This is the accepted manuscript version of the article. The final version is available online from IEEE at: https://doi.org/10.1109/CVPR46437.2021.00047
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