Official PyTorch implementation of "Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning".
[2023.5.21] Pretrained models have been released in MODEL_ZOO.md.
[2023.4.9] Code of MVD is available now!
[2023.2.28] MVD is accepted by CVPR 2023.
| Method | Pretrain Video Data | Backbone | Teacher | Epoch | Top-1 | Top-5 | resolution | #Frames x Clips x Crops | Param |
|---|---|---|---|---|---|---|---|---|---|
| MVD | Kinetics-400 | ViT-S | ViT-B | 400 | 70.7 | 92.6 | 224 | 16x2x3 | 22M |
| MVD | Kinetics-400 | ViT-S | ViT-L | 400 | 70.9 | 92.8 | 224 | 16x2x3 | 22M |
| MVD | Kinetics-400 | ViT-B | ViT-B | 400 | 72.5 | 93.6 | 224 | 16x2x3 | 87M |
| MVD | Kinetics-400 | ViT-B | ViT-L | 400 | 73.7 | 94.0 | 224 | 16x2x3 | 87M |
| MVD | Kinetics-400 | ViT-L | ViT-L | 400 | 76.1 | 95.4 | 224 | 16x2x3 | 305M |
| MVD | Kinetics-400 | ViT-L | ViT-L | 800 | 76.7 | 95.5 | 224 | 16x2x3 | 305M |
| MVD | Kinetics-400 | ViT-H | ViT-H | 800 | 77.3 | 95.7 | 224 | 16x2x3 | 633M |
| Method | Pretrain Video Data | Backbone | Teacher | Epoch | Top-1 | Top-5 | resolution | #Frames x Clips x Crops | Param |
|---|---|---|---|---|---|---|---|---|---|
| MVD | Kinetics-400 | ViT-S | ViT-B | 400 | 80.6 | 94.7 | 224 | 16x5x3 | 22M |
| MVD | Kinetics-400 | ViT-S | ViT-L | 400 | 81.0 | 94.8 | 224 | 16x5x3 | 22M |
| MVD | Kinetics-400 | ViT-B | ViT-B | 400 | 82.7 | 95.4 | 224 | 16x5x3 | 87M |
| MVD | Kinetics-400 | ViT-B | ViT-L | 400 | 83.4 | 95.8 | 224 | 16x5x3 | 87M |
| MVD | Kinetics-400 | ViT-L | ViT-L | 400 | 86.0 | 96.9 | 224 | 16x5x3 | 305M |
| MVD | Kinetics-400 | ViT-L | ViT-L | 800 | 86.4 | 97.0 | 224 | 16x5x3 | 305M |
| MVD | Kinetics-400 | ViT-H | ViT-H | 800 | 87.3 | 97.4 | 224 | 16x5x3 | 633M |
| Method | Pretrain Video Data | Extra Label | Backbone | Teacher | Epoch | mAP | #Frames x Sample Rate | Param |
|---|---|---|---|---|---|---|---|---|
| MVD | Kinetics-400 | ✗ | ViT-B | ViT-B | 400 | 29.3 | 16x4 | 87M |
| MVD | Kinetics-400 | ✓ | ViT-B | ViT-B | 400 | 33.6 | 16x4 | 87M |
| MVD | Kinetics-400 | ✗ | ViT-B | ViT-L | 400 | 31.1 | 16x4 | 87M |
| MVD | Kinetics-400 | ✓ | ViT-B | ViT-L | 400 | 34.2 | 16x4 | 87M |
| MVD | Kinetics-400 | ✗ | ViT-L | ViT-L | 800 | 37.7 | 16x4 | 305M |
| MVD | Kinetics-400 | ✓ | ViT-L | ViT-L | 800 | 38.7 | 16x4 | 305M |
| MVD | Kinetics-400 | ✗ | ViT-H | ViT-H | 800 | 40.1 | 16x4 | 633M |
| MVD | Kinetics-400 | ✓ | ViT-H | ViT-H | 800 | 41.1 | 16x4 | 633M |
| Method | Pretrain Video Data | Backbone | Teacher | Epoch | UCF101 Top-1 | HMDB51 Top-1 |
|---|---|---|---|---|---|---|
| MVD | Kinetics-400 | ViT-B | ViT-B | 400 | 97.0 | 76.4 |
| MVD | Kinetics-400 | ViT-B | ViT-L | 400 | 97.5 | 79.7 |
Please follow the instructions in INSTALL.md.
Please follow the instructions in DATASET.md for data preparation.
The pre-training instruction is in PRETRAIN.md.
The fine-tuning instruction is in FINETUNE.md.
We provide pre-trained models in MODEL_ZOO.md.
This project is built upon MAE and VideoMAE. Thanks to the contributors of these great codebases.
If this work is helpful for your research, please consider citing MVD.
@inproceedings{wang2022masked,
title={Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning},
author={Wang, Rui and Chen, Dongdong and Wu, Zuxuan and Chen, Yinpeng and Dai, Xiyang and Liu, Mengchen and Yuan, Lu and Jiang, Yu-Gang},
booktitle={CVPR},
year={2023}
}
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