From 7774aedd09098f2d2a853c85263190e9d1de4f70 Mon Sep 17 00:00:00 2001 From: ChunyuanLI Date: Thu, 24 Nov 2022 22:43:46 -0800 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b42505d..fbef543 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ We make the comparisons between UniCL with coventional learning methods below:

## Updates - +* [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.