docs/intro.md

["**Unifiled Contrastive Learning in Image-Text-Label Space. CVPR 2022**"](https://arxiv.org/abs/2204.03610) by [Jianwei Yang*](https://jwyang.github.io/), [Chunyuan Li*](https://chunyuan.li/), [Pengchuan Zhang*](https://pzzhang.github.io/pzzhang/), [Bin Xiao*](https://www.microsoft.com/en-us/research/people/bixi/), [Ce Liu](http://people.csail.mit.edu/celiu/), [Lu Yuan](https://scholar.google.com/citations?user=k9TsUVsAAAAJ&hl=en) and [Jianfeng Gao](https://www.microsoft.com/en-us/research/people/jfgao/?from=http%3A%2F%2Fresearch.microsoft.com%2Fen-us%2Fum%2Fpeople%2Fjfgao%2F).

In this paper, we introduce a new perspective on commonly used image-label and image-text data by residing them in an image-text-label space. In this space, a new learning paradigm, called **Unified Contrastive Learning (UniCL)** with a single learning objective is proposed to seamlessly prompt the synergy of two data types. We demonstrate that UniCL is an effective way of learning **semantically rich yet discriminative representations**, universally for image recognition in zero-shot, linear-probe, fully finetuning and transfer learning scenarios. When scaled up to billions of data, UniCL can exclusively learn a powerful visual-semantic representation supporting dozens of downstream tasks shown in [Florence](https://arxiv.org/pdf/2111.11432v1.pdf).