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TransMix: attend to mix for Vision Transformers

Abstract:

Mixup-based augmentation has been found to be effective for generalizing models during training, especially for Vision Transformers (ViTs) since they can easily overfit. However, previous mixup-based methods have an underlying prior knowledge that the linearly interpolated ratio of targets should be kept the same as the ratio proposed in input interpolation. This may lead to a strange phenomenon that sometimes there is no valid object in the mixed image due to the random process in augmentati...

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Publication status:
Accepted
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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John Fell Fund More from this funder
Publisher:
IEEE Publisher's website
Acceptance date:
2022-06-19
Event title:
Conference on Computer Vision and Pattern Recognition (CVPR 2022)
Event location:
New Orleans, Louisiana
Event website:
https://cvpr2022.thecvf.com/
Event start date:
2022-06-19T00:00:00Z
Event end date:
2022-06-24T00:00:00Z
Language:
English
Keywords:
Pubs id:
1272328
Local pid:
pubs:1272328
Deposit date:
2022-08-03

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