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Method evidence record

CLIP

CLIP (Contrastive Language-Image Pretraining) is a vision-language model introduced by Radford et al. at OpenAI in 2021 that jointly learns aligned image and text representations by training on 400 million internet-sourced image-text pairs using a contrastive objective, enabling zero-shot transfer to image classification tasks without any task-specific fine-tuning.

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Contrastive Language-Image Pretraining

Taxonomic method recordml-modeldeep-learning

  1. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning, PMLR 139, 8748–8763. · URL

  2. Radford, A., et al. (2021). Learning Transferable Visual Models From Natural Language Supervision. arXiv:2103.00020. · URL

  3. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. · ISBN 978-0-262-03561-3

Open full method3 recorded citations, copied from the method source record.

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