![]() Learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. After pre-training, natural language is used to reference Million (image, text) pairs collected from the internet. With which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 ![]() We demonstrate that the simple pre-training task of predicting which caption goes Learning directly from raw text about images is a promising alternative which leverages a Restricted form of supervision limits their generality and usability since additional labeled data is needed to specifyĪny other visual concept. State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. The abstract from the paper is the following: Instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizingįor the task, similarly to the zero-shot capabilities of GPT-2 and 3. (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever. The CLIP model was proposed in Learning Transferable Visual Models From Natural Language Supervision by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh,
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