diff --git a/README.md b/README.md
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@@ -44,6 +44,7 @@ The master branch works with **PyTorch 1.3+**.
 
 ## Updates
 
+- (2021-11-24) We support **2s-AGCN** on NTU60 XSub, achieve 86.82% Top-1 accuracy on joint stream and 87.91% Top-1 accuracy on bone stream respectively.
 - (2021-10-29) We provide a demo for skeleton-based and rgb-based spatio-temporal detection and action recognition (demo/demo_video_structuralize.py).
 - (2021-10-26) We train and test **ST-GCN** on NTU60 with 3D keypoint annotations, achieve 84.61% Top-1 accuracy (higher than 81.5% in the [paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/17135)).
 - (2021-10-25) We provide a script(tools/data/skeleton/gen_ntu_rgbd_raw.py) to convert the NTU60 and NTU120 3D raw skeleton data to our format.
diff --git a/README_zh-CN.md b/README_zh-CN.md
index e9ee522b9d..05550aae20 100644
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@@ -43,6 +43,7 @@ MMAction2 是一款基于 PyTorch 的视频理解开源工具箱，是 [OpenMMLa
 
 ## 更新记录
 
+- (2021-11-24) 在 NTU60 XSub 上支持 **2s-AGCN**， 在 joint stream 和 bone stream 上分别达到 86.82% 和 87.91% 的识别准确率。
 - (2021-10-29) 支持基于 skeleton 模态和 rgb 模态的时空动作检测和行为识别 demo (demo/demo_video_structuralize.py)。
 - (2021-10-26) 在 NTU60 3d 关键点标注数据集上训练测试 **STGCN**, 可达到 84.61% (高于 [paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/17135) 中的 81.5%) 的识别准确率。
 - (2021-10-25) 提供将 NTU60 和 NTU120 的 3d 骨骼点数据转换成我们项目的格式的脚本(tools/data/skeleton/gen_ntu_rgbd_raw.py)。