| # Ecosystem Projects based on MMAction2 | |
| There are many research works and projects built on MMAction2. | |
| We list some of them as examples of how to extend MMAction2 for your own projects. | |
| As the page might not be completed, please feel free to create a PR to update this page. | |
| ## Projects as an extension | |
| - [OTEAction2](https://github.com/openvinotoolkit/mmaction2): OpenVINO Training Extensions for Action Recognition. | |
| - [PYSKL](https://github.com/kennymckormick/pyskl): A Toolbox Focusing on Skeleton-Based Action Recognition. | |
| ## Projects of papers | |
| There are also projects released with papers. | |
| Some of the papers are published in top-tier conferences (CVPR, ICCV, and ECCV), the others are also highly influential. | |
| To make this list also a reference for the community to develop and compare new video understanding algorithms, we list them following the time order of top-tier conferences. | |
| Methods already supported and maintained by MMAction2 are not listed. | |
| - Video Swin Transformer, CVPR 2022. [\[paper\]](https://arxiv.org/abs/2106.13230)[\[github\]](https://github.com/SwinTransformer/Video-Swin-Transformer) | |
| - Evidential Deep Learning for Open Set Action Recognition, ICCV 2021 Oral. [\[paper\]](https://arxiv.org/abs/2107.10161)[\[github\]](https://github.com/Cogito2012/DEAR) | |
| - Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective, ICCV 2021 Oral. [\[paper\]](https://arxiv.org/abs/2103.17263)[\[github\]](https://github.com/xvjiarui/VFS) | |
| - MGSampler: An Explainable Sampling Strategy for Video Action Recognition, ICCV 2021. [\[paper\]](https://arxiv.org/abs/2104.09952)[\[github\]](https://github.com/MCG-NJU/MGSampler) | |
| - MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions, ICCV 2021. [\[paper\]](https://arxiv.org/abs/2105.07404) | |
| - Long Short-Term Transformer for Online Action Detection, NeurIPS 2021 [\[paper\]](https://arxiv.org/abs/2107.03377)[\[github\]](https://github.com/amazon-research/long-short-term-transformer) | |