Update docs/intro.md

docs/intro.md

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-# [Unified Contrastive Learning in Image-Text-Label Space](https://arxiv.org/abs/2204.03610)
 
 ["**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).
 
-## Motivation
-
-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).
-
-## Benchmarking
-
-### Image-label training augmented by image-text pairs
-
-| Model | Training Set | Top-1 on IN-1K | ZS on 14 datasets | Download
-| :----: | :---: | :---: | :---: | :---: |
-| Swin-T | IN-1K | 79.9 | 30.2 | [ckpt](https://projects4jw.blob.core.windows.net/unicl/release/in1k.pth)/[config](configs/unicl_swin_tiny.yaml)
-| Swin-T | IN-1K + GCC-3M | 80.2 | 39.0 | [ckpt](https://projects4jw.blob.core.windows.net/unicl/release/in1k_gcc3m.pth)/[config](configs/unicl_swin_tiny.yaml)
-| Swin-T | IN-1K + GYFCC-14M | 81.1 | 40.0 | [ckpt](https://projects4jw.blob.core.windows.net/unicl/release/in1k_yfcc14m.pth)/[config](configs/unicl_swin_tiny.yaml)
-| Swin-T | IN-1K + GCC-15M | 81.8 | 45.1 | [ckpt](https://projects4jw.blob.core.windows.net/unicl/release/in1k_gcc15m.pth)/[config](configs/unicl_swin_tiny.yaml)
-
-Note that all the above models are trained **without** strong data augmentations like mixup and cutmix.
-
-### Image-text learning augmented by image-label data
-
-| Model | Training Set | ZS on IN-1K | ZS on 14 datasets | Download
-| :----: | :---: | :---: | :---: | :---: |
-| Swin-T | YFCC-14M | 30.1 | 36.3 | [ckpt](https://projects4jw.blob.core.windows.net/unicl/release/yfcc14m.pth)/[config](configs/unicl_swin_tiny.yaml)
-| Swin-T | IN-21K | 28.5 | 37.8 | [ckpt](https://projects4jw.blob.core.windows.net/unicl/release/in21k.pth)/[config](configs/unicl_swin_tiny.yaml)
-| Swin-T | IN-21K (half) + YFCC-14M (half) | 36.4 | 45.5 | [ckpt](https://projects4jw.blob.core.windows.net/unicl/release/in21k_yfcc14m_half.pth)/[config](configs/unicl_swin_tiny.yaml)
-| Swin-T | IN-21K + YFCC-14M | 40.5 | 49.1 | [ckpt](https://projects4jw.blob.core.windows.net/unicl/release/in21k_yfcc14m.pth)/[config](configs/unicl_swin_tiny.yaml)
-| Swin-B | YFCC-14M | 37.8 | - | [ckpt](https://projects4jw.blob.core.windows.net/unicl/release/yfcc14m_swin_base.pth)/[config](configs/unicl_swin_base.yaml)
-| Swin-B | IN-21K | 29.9 | 42.4 | [ckpt](https://projects4jw.blob.core.windows.net/unicl/release/in21k_swin_base.pth)/[config](configs/unicl_swin_base.yaml)
-| Swin-B | IN-21K (half) + YFCC-14M (half) | 41.1 | 48.5 | [ckpt](https://projects4jw.blob.core.windows.net/unicl/release/in21k_yfcc14m_half_swin_base.pth)/[config](configs/unicl_swin_base.yaml)
-| Swin-B | IN-21K + YFCC-14M | 44.3 | 52.2 | [ckpt](https://projects4jw.blob.core.windows.net/unicl/release/in21k_yfcc14m_swin_base.pth)/[config](configs/unicl_swin_base.yaml)
-| Swin-B | IN-21K + YFCC-14M + GCC-15M | 57.9 | - | [ckpt](https://projects4jw.blob.core.windows.net/unicl/release/in21k_yfcc14m_gcc15m_swin_base.pth)/[config](configs/unicl_swin_base.yaml)
+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).