https://github.com/xiaojieli0903/maskagain
Official repository of the “Mask Again: Masked Knowledge Distillation for Masked Video Modeling” (ACM MM 2023)
https://github.com/xiaojieli0903/maskagain
knowledge-distillation masked-video-modeling video-representation-learning
Last synced: 1 day ago
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Official repository of the “Mask Again: Masked Knowledge Distillation for Masked Video Modeling” (ACM MM 2023)
- Host: GitHub
- URL: https://github.com/xiaojieli0903/maskagain
- Owner: xiaojieli0903
- License: other
- Created: 2022-11-26T13:57:06.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-07-11T04:35:57.000Z (11 months ago)
- Last Synced: 2025-04-12T22:30:07.029Z (2 months ago)
- Topics: knowledge-distillation, masked-video-modeling, video-representation-learning
- Language: Python
- Homepage: https://dl.acm.org/doi/10.1145/3581783.3612129
- Size: 8.05 MB
- Stars: 27
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Official PyTorch Implementation of Mask Again: Masked Knowledge Distillation for Masked Video Modeling (ACM MM 2023).
> [**Mask Again: Masked Knowledge Distillation for Masked Video Modeling**](https://dl.acm.org/doi/10.1145/3581783.3612129)[ [PDF]](https://github.com/xiaojieli0903/MaskAgain/blob/master/Mask%20Again%20Masked%20Knowledge%20Distillation%20for%20Masked%20Video.pdf)
> [Xiaojie Li](https://github.com/xiaojieli0903)^1,2, Shaowei He^1, [Jianlong Wu](https://jlwu1992.github.io)^1*, [Yue Yu](https://yuyue.github.io)^2, [Liqiang Nie](https://liqiangnie.github.io)^1*, [Min Zhang](https://zhangminsuda.github.io)^1
> ^1Harbin Institute of Technology, Shenzhen, ^2Peng Cheng Laboratory
> *Corresponding Author
## 🚀 Main Results
### ✨ Kinetics-400
| Method | Extra Data | Backbone | Resolution | #Frames x Clips x Crops | Top-1 | Top-5 |
| :------: | :--------: | :------: | :--------: | :---------------------: | :---: | :---: |
| VideoMAE | ***no*** | ViT-S | 224x224 | 16x5x3 | 78.7 | 93.6 |
| VideoMAE | ***no*** | ViT-B | 224x224 | 16x5x3 | 81.0 | 94.6 |
### ✨ UCF101 & HMDB51
| Method | Extra Data | Backbone | UCF101 | HMDB51 |
| :------: | :----------: | :------: | :----: | :----: |
| VideoMAE | Kinetics-400 | ViT-S | 92.9 | 72.0 |
| VideoMAE | Kinetics-400 | ViT-B | 96.2 | 77.1 |
## 🔨 Installation
Please follow the instructions in [INSTALL.md](INSTALL.md).
## 📍Model Zoo
We provide pre-trained and fine-tuned models in [MODEL_ZOO.md](MODEL_ZOO.md).
## 👀 Visualization
We provide the script for visualization in [`vis_kd.sh`](scripts/vis_kd.sh).
## ✏️ Citation
If you find this project useful for your research, please considering leaving a star⭐️ and citing our paper:
```
@inproceedings{li2023mask,
title={Mask Again: Masked Knowledge Distillation for Masked Video Modeling},
author={Li, Xiaojie and He, Shaowei and Wu, Jianlong and Yu, Yue and Nie, Liqiang and Zhang, Min},
booktitle={Proceedings of the 31st ACM International Conference on Multimedia},
pages={2221--2232},
year={2023}
}
```
## 🔒 License
This project is made available under the [Apache 2.0 license](LICENSE).
## 👍 Acknowledgements
This project is built upon [VideoMAE](https://github.com/MCG-NJU/VideoMAE.git). Thanks to the contributors of this great codebase.