Create README.md
Browse files
README.md
ADDED
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
license: mit
|
3 |
+
language:
|
4 |
+
- en
|
5 |
+
---
|
6 |
+
|
7 |
+
# Grounded-VideoLLM Model Card
|
8 |
+
Grounded-VideoLLM is a Video-LLM adept at fine-grained temporal grounding, which not only excels in grounding tasks such as temporal sentence grounding, dense video captioning, and grounded VideoQA, but also shows great potential as a versatile video assistant for general video understanding.
|
9 |
+
|
10 |
+
## Model details
|
11 |
+
|
12 |
+
**Model date:**
|
13 |
+
Grounded-VideoLLM-Phi3.5-Vision-Instruct was trained in Oct. 2024.
|
14 |
+
|
15 |
+
**Paper or resources for more information:**
|
16 |
+
[Paper](https://arxiv.org/abs/2410.03290), [Code](https://github.com/WHB139426/Grounded-Video-LLM)
|
17 |
+
|
18 |
+
## Citation
|
19 |
+
If you find our project useful, hope you can star our repo and cite our paper as follows:
|
20 |
+
|
21 |
+
```
|
22 |
+
@misc{wang2024groundedvideollm,
|
23 |
+
title={Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models},
|
24 |
+
author={Haibo Wang and Zhiyang Xu and Yu Cheng and Shizhe Diao and Yufan Zhou and Yixin Cao and Qifan Wang and Weifeng Ge and Lifu Huang},
|
25 |
+
year={2024},
|
26 |
+
eprint={2410.03290},
|
27 |
+
archivePrefix={arXiv},
|
28 |
+
primaryClass={cs.CV}
|
29 |
+
}
|
30 |
+
```
|