Learning Transferable Visual Models From Natural Language Supervision We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet
Learning Transferable Visual Models From Natural Language Supervision This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision
Learning Transferable Visual Models From Natural Language Supervision Learning Transferable Visual Models From Natural Language Supervision Alec Radford · Jong Wook Kim · Chris Hallacy · Aditya Ramesh · Gabriel Goh · Sandhini Agarwal · Girish Sastry · Amanda Askell · Pamela Mishkin · Jack Clark · Gretchen Krueger · Ilya Sutskever
【大模型学习 | CLIP 原理 实现】-腾讯云开发者社区-腾讯云 Learning Transferable Visual Models From Natural Language Supervision 作者在摘要中指出，传统的监督式学习方法限制了视觉模型的泛化能力，特别是在迁移到新任务或新类别时的能力有限。以往的 图像识别 任务通常依赖于人为定义的分类标签进行训练，这种方式不仅数据成本高，而且模型更容易过拟合于训练类别
【论文翻译】Learning Transferable Visual Models From Natural Language Supervision Although early work wrestled with the complexity of natural language when using topic model and n-gram representations, improvements in deep contextual representation learning suggest we now have the tools to effectively leverage this abundant source of supervision (McCann et al ,2017)