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This paper presents a simple yet effective framework MaskCLIP, which incorporates a newly proposed masked self-distillation into contrastive language-image pretraining.
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Big self-supervised models are strong semi-supervised learners
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Improved baselines with momentum contrastive learning
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Masked feature prediction for self-supervised visual pre-training
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E2e-vlp: End-to-end vision-language pre-training enhanced by visual learning
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General facial representation learning in a visual-linguistic manner
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Data2vec: A general framework for self-supervised learning in speech, vision and language
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Context autoencoder for self-supervised representation learning
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Bootstrapped masked autoencoders for vision bert pretraining
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K-lite: Learning transferable visual models with external knowledge
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Efficient vision-language pretraining with visual concepts and hierarchical alignment
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