Learning Transferable Visual Models From Natural Language ...
Learning Transferable Visual Models From Natural Language SupervisionAlec Radford* 1Jong Wook Kim* 1Chris Hallacy1Aditya Ramesh1Gabriel Goh1Sandhini Agarwal1Girish Sastry1Amanda Askell1Pamela Mishkin1Jack Clark1Gretchen Krueger1Ilya Sutskever1AbstractState-of-the-art computer vision systems aretrained to predict a fixed set of predeterminedobject categories. This restricted form of super-vision limits their generality and usability sinceadditional labeled data is needed to specify anyother Visual concept. Learning directly from rawtext about images is a promising alternative whichleverages a much broader source of demonstrate that the simple pre-training taskof predicting which caption goes with which im-age is an efficient and scalable way to learn SOTAimage representations from sc
have improved performance.Mahajan et al.(2018) showed that predicting ImageNet-related hashtags on Instagram im-ages is an effective pre-training task. When fine-tuned to ImageNet these pre-trained models increased accuracy by over 5% and improved the overall state of the art at the time. Kolesnikov et al.(2019) andDosovitskiy et al.(2020) have
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