Learning Transferable Visual Models From Natural Language Supervision
Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive licence to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2103.00020
Summary
Most computer vision systems learn a fixed list of categories, so each new concept needs more labeled data. The authors test whether the web-scale, text-driven pre-training that changed NLP can bring similar gains to vision. They collect WIT, a set of 400 million image–text pairs from the internet. On it they train CLIP (Contrastive Language-Image Pre-training), a simplified version of ConVIRT. CLIP has an image encoder and a text encoder, and training teaches them to give the correct image–caption pairs in a batch higher similarity than the mismatched pairs. At test time, the text encoder embeds class names or short prompts, which yields a classifier without any dataset-specific training. The authors evaluate CLIP zero-shot on more than 30 benchmarks, covering OCR, video action recognition, geo-localization and fine-grained classification. CLIP frequently rivals fully supervised baselines and is clearly more robust than ImageNet models of the same accuracy. The work shows that natural language can act as a scalable and flexible supervision source for visual models. Code and weights are public.
Key points
- Dataset: WIT contains 400M (image, text) pairs. For comparison, YFCC100M shrinks to about 15M usable photos after filtering for English natural-language metadata.
- ImageNet result: zero-shot CLIP matches the accuracy of the original ResNet-50 without using any of its 1.28M training images. Earlier zero-shot work (Li et al., 2017) reached only 11.5%.
- Training efficiency:
- The contrastive objective is about 4x more efficient for zero-shot ImageNet than predicting a bag-of-words encoding of the text.
- Bag-of-words prediction is in turn about 3x more efficient than a transformer caption language model.
- Scaling: eight models span roughly two orders of magnitude of compute. Transfer performance grows smoothly and predictably with compute.
- Design and release: the only image–text interaction is a single dot product in a shared embedding space. Code and pre-trained weights are released at github.com/OpenAI/CLIP.
Cited by
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- A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity
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- Language Is Not All You Need: Aligning Perception with Language Models
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- BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
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Cite
@misc{radford2021learning,
title = {{Learning Transferable Visual Models From Natural Language Supervision}},
author = {Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and Krueger, Gretchen and Sutskever, Ilya},
year = {2021},
doi = {10.48550/arxiv.2103.00020},
eprint = {2103.00020},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2103.00020}
}References
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