MetaCLIP 2: A Worldwide Scaling Recipe
Contrastive Language-Image Pretraining (CLIP) is a popular foundation model,
supporting from zero-shot classification, retrieval to encoders for multimodal
large language models (MLLMs). Although CLIP is successfully trained on
billion-scale image-text pairs from the English world, scaling CLIP's training
further to learning from the worldwide web data is still challenging: (1) no
curation method is available to handle data points from non-English world; (2)
the English performance from existing multilingual CLIP is worse than its
English-only counterpart, i.e., "curse of multilinguality" that is common in
LLMs. Here, we present MetaCLIP 2, the first recipe training CLIP from scratch
on worldwide web-scale image-text pairs. To generalize our findings, we conduct
rigorous ablations with minimal changes that are necessary to address the above
challenges and present a recipe enabling mutual benefits from English and
non-English world data. In zero-shot ImageNet classification, MetaCLIP 2
ViT-H/14 surpasses its English-only counterpart by 0.8% and mSigLIP by 0.7%,
and surprisingly sets new state-of-the-art without system-level confounding
factors (e.g., translation, bespoke architecture changes) on multilingual
benchmarks, such as CVQA with 57.4%, Babel-ImageNet with 50.2% and XM3600 with
64.3% on image-to-text retrieval.
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Yung-Sung Chuang
claimed_verified
2025-07-31T21:00:22.011Z
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Yung-Sung Chuang
user
voidism
688b6429292830d55fa68a2d
Yang Li
claimed_verified
2025-08-06T19:31:55.479Z
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Yang
user
yli1
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Dong Wang
claimed_verified
2025-08-06T19:31:51.198Z
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Dong Wang
user
dongwang218
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Ching-Feng Yeh
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Kehan Lyu
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Ramya Raghavendra
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James Glass
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Lifei Huang
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Jason Weston
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Luke Zettlemoyer
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Xinlei Chen
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Zhuang Liu
688b6429292830d55fa68a38
Saining Xie
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Wen-tau Yih
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Shang-Wen Li
claimed_verified
2025-08-06T19:31:58.606Z
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Shang-Wen Daniel Li
user
swdanielli
688b6429292830d55fa68a3b
Hu Xu
MetaCLIP 2: A Worldwide Scaling Recipe
Contrastive Language-Image Pretraining (CLIP) is a popular foundation model,
supporting from zero-shot classification, retrieval to encoders for multimodal
large language models (MLLMs). Although CLIP is successfully trained on
billion-scale image-text pairs from the English world, scaling CLIP's training
further to learning from the worldwide web data is still challenging: (1) no
curation method is available to handle data points from non-English world; (2)
the English performance from existing multilingual CLIP is worse than its
English-only counterpart, i.e., "curse of multilinguality" that is common in
LLMs. Here, we present MetaCLIP 2, the first recipe training CLIP from scratch
on worldwide web-scale image-text pairs. To generalize our findings, we conduct
rigorous ablations with minimal changes that are necessary to address the above
challenges and present a recipe enabling mutual benefits from English and
non-English world data. In zero-shot ImageNet classification, MetaCLIP 2
ViT-H/14 surpasses its English-only counterpart by 0.8% and mSigLIP by 0.7%,
and surprisingly sets new state-of-the-art without system-level confounding
factors (e.g., translation, bespoke architecture changes) on multilingual
benchmarks, such as CVQA with 57.4%, Babel-ImageNet with 50.2% and XM3600 with
64.3% on image-to-text retrieval.
MetaCLIP 2, trained on worldwide web-scale image-text pairs, improves zero-shot classification and multilingual benchmarks without system-level confounding factors.
Contrastive Language-Image Pretraining CLIP multilingual CLIP MetaCLIP 2 zero-shot classification image-text pairs multilingual benchmarks CVQA Babel-ImageNet XM3600 image-to-text retrieval