Improving CLIP Training with Language Rewrites
Contrastive Language-Image Pre-training (CLIP) stands as one of the most
effective and scalable methods for training transferable vision models using
paired image and text data. CLIP models are trained using contrastive loss,
which typically relies on data augmentations to prevent overfitting and
shortcuts. However, in the CLIP training paradigm, data augmentations are
exclusively applied to image inputs, while language inputs remain unchanged
throughout the entire training process, limiting the exposure of diverse texts
to the same image. In this paper, we introduce Language augmented CLIP
(LaCLIP), a simple yet highly effective approach to enhance CLIP training
through language rewrites. Leveraging the in-context learning capability of
large language models, we rewrite the text descriptions associated with each
image. These rewritten texts exhibit diversity in sentence structure and
vocabulary while preserving the original key concepts and meanings. During
training, LaCLIP randomly selects either the original texts or the rewritten
versions as text augmentations for each image. Extensive experiments on CC3M,
CC12M, RedCaps and LAION-400M datasets show that CLIP pre-training with
language rewrites significantly improves the transfer performance without
computation or memory overhead during training. Specifically for ImageNet
zero-shot accuracy, LaCLIP outperforms CLIP by 8.2% on CC12M and 2.4% on
LAION-400M. Code is available at https://github.com/LijieFan/LaCLIP.
64780dbf312e3909019a8766
Lijie Fan
64780dbf312e3909019a8767
Dilip Krishnan
64780dbf312e3909019a8768
Phillip Isola
admin_assigned
2023-06-01T08:37:01.067Z
623cb1f3ca87989bbf797eee
/avatars/13fbd5b123b8676561751f820317600c.svg
Phillip Isola
user
phillipi
64780dbf312e3909019a8769
Dina Katabi
64780dbf312e3909019a876a
Yonglong Tian
claimed_verified
2023-06-02T07:16:38.600Z
64793086ac77bb7603842456
/avatars/5891292138d372a9cc063f86c08b8174.svg
Yonglong Tian
user
tyl5566
Improving CLIP Training with Language Rewrites
Contrastive Language-Image Pre-training (CLIP) stands as one of the most
effective and scalable methods for training transferable vision models using
paired image and text data. CLIP models are trained using contrastive loss,
which typically relies on data augmentations to prevent overfitting and
shortcuts. However, in the CLIP training paradigm, data augmentations are
exclusively applied to image inputs, while language inputs remain unchanged
throughout the entire training process, limiting the exposure of diverse texts
to the same image. In this paper, we introduce Language augmented CLIP
(LaCLIP), a simple yet highly effective approach to enhance CLIP training
through language rewrites. Leveraging the in-context learning capability of
large language models, we rewrite the text descriptions associated with each
image. These rewritten texts exhibit diversity in sentence structure and
vocabulary while preserving the original key concepts and meanings. During
training, LaCLIP randomly selects either the original texts or the rewritten
versions as text augmentations for each image. Extensive experiments on CC3M,
CC12M, RedCaps and LAION-400M datasets show that CLIP pre-training with
language rewrites significantly improves the transfer performance without
computation or memory overhead during training. Specifically for ImageNet
zero-shot accuracy, LaCLIP outperforms CLIP by 8.2% on CC12M and 2.4% on
LAION-400M. Code is available at https://github.com/LijieFan/LaCLIP.
Language augmented CLIP (LaCLIP) enhances CLIP training by introducing text rewrites, improving zero-shot accuracy without increasing computational costs.
Contrastive Language-Image Pre-training CLIP contrastive loss data augmentations image inputs language inputs LaCLIP in-context learning large language models text rewrites CC3M CC12M RedCaps LAION-400M ImageNet zero-shot accuracy