Update README.md
Browse files
README.md
CHANGED
|
@@ -1,3 +1,49 @@
|
|
| 1 |
---
|
| 2 |
license: apache-2.0
|
| 3 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
---
|
| 2 |
license: apache-2.0
|
| 3 |
---
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
<br>
|
| 8 |
+
|
| 9 |
+
# VideoMamba
|
| 10 |
+
|
| 11 |
+
## Model Details
|
| 12 |
+
|
| 13 |
+
VideoMamba is a purely SSM-based model for video understanding.
|
| 14 |
+
|
| 15 |
+
- **Developed by:** [OpenGVLab](https://github.com/OpenGVLab)
|
| 16 |
+
- **Model type:** An efficient backbone based on the bidirectional state space model.
|
| 17 |
+
- **License:** Non-commercial license
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
### Model Sources
|
| 21 |
+
|
| 22 |
+
- **Repository:** https://github.com/OpenGVLab/VideoMamba
|
| 23 |
+
- **Paper:** https://arxiv.org/abs/2403.06977
|
| 24 |
+
|
| 25 |
+
## Uses
|
| 26 |
+
|
| 27 |
+
The primary use of VideoMamba is research on image and video tasks, e.g., image classification, action recognition, long-term video understanding, and video-text retrieval, with an SSM-based backbone.
|
| 28 |
+
The primary intended users of the model are researchers and hobbyists in computer vision, machine learning, and artificial intelligence.
|
| 29 |
+
|
| 30 |
+
## How to Get Started with the Model
|
| 31 |
+
|
| 32 |
+
- You can replace the backbone for video tasks with the proposed VideoMamba: https://github.com/OpenGVLab/VideoMamba/blob/main/videomamba/video_sm/models/videomamba.py
|
| 33 |
+
- Then you can load this checkpoint and start training.
|
| 34 |
+
|
| 35 |
+
|
| 36 |
+
### Citation Information
|
| 37 |
+
|
| 38 |
+
```
|
| 39 |
+
@misc{li2024videomamba,
|
| 40 |
+
title={VideoMamba: State Space Model for Efficient Video Understanding},
|
| 41 |
+
author={Kunchang Li and Xinhao Li and Yi Wang and Yinan He and Yali Wang and Limin Wang and Yu Qiao},
|
| 42 |
+
year={2024},
|
| 43 |
+
eprint={2403.06977},
|
| 44 |
+
archivePrefix={arXiv},
|
| 45 |
+
primaryClass={cs.CV}
|
| 46 |
+
}
|
| 47 |
+
```
|
| 48 |
+
|
| 49 |
+
|