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 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/revisiting-skeleton-based-action-recognition/skeleton-based-action-recognition-on-ntu-rgbd)](https://paperswithcode.com/sota/skeleton-based-action-recognition-on-ntu-rgbd?p=revisiting-skeleton-based-action-recognition)
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/dg-stgcn-dynamic-spatial-temporal-modeling/skeleton-based-action-recognition-on-ntu-rgbd-1)](https://paperswithcode.com/sota/skeleton-based-action-recognition-on-ntu-rgbd-1?p=dg-stgcn-dynamic-spatial-temporal-modeling)
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/revisiting-skeleton-based-action-recognition/skeleton-based-action-recognition-on-kinetics)](https://paperswithcode.com/sota/skeleton-based-action-recognition-on-kinetics?p=revisiting-skeleton-based-action-recognition)
-[Report](https://arxiv.org/abs/2205.09443)
+[[**Report**]](https://arxiv.org/abs/2205.09443)
 
 PYSKL is a toolbox focusing on action recognition based on **SK**e**L**eton data with **PY**Torch. Various algorithms will be supported for skeleton-based action recognition. We build this project based on the OpenSource Project [MMAction2](https://github.com/open-mmlab/mmaction2).