Update README.md

Author: ChunyuanLI

README.md

@@ -19,7 +19,7 @@ We make the comparisons between UniCL with coventional learning methods below:
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 ## Updates
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+* [11/24/2022] [KLITE](https://arxiv.org/abs/2204.09222), the knowledge-augmented version of UniCL, is publicly released on [Github](https://github.com/microsoft/klite).
 * :collision: [10/05/2022] How do we use the pretrainied UniCL checkpoints? Beyond the zero-shot classification shown in our paper, we can use them for object detection. Now [RegionCLIP](https://github.com/microsoft/RegionCLIP/tree/zero-shot) supports to use pretrained UniCL transformer models, such as Swin, ViT for open-vocabulary object detection without any finetuning. Check it out!
 * [08/19/2022] Organizing ECCV Workshop [*Computer Vision in the Wild (CVinW)*](https://computer-vision-in-the-wild.github.io/eccv-2022/), where two challenges are hosted to evaluate the zero-shot, few-shot and full-shot performance of pre-trained vision models in downstream tasks:
   - [``*Image Classification in the Wild (ICinW)*''](https://eval.ai/web/challenges/challenge-page/1832/overview) Challenge evaluates on 20 image classification tasks.