Vision-CAIR/VLV-Benchmark

# VLV-Bench: A Comprehensive benchmark for very long-form videos understanding
# How to download videos
1- TVQA videos <br>
2- MovieNet Data

# Annotation files
You can find the annotation files for the 9 skills here
# How to create the Benchmark
## Data scrapping
1- We scrapped the all the TVQA summaries from IMDB. <br>
2- We scrapped the all the MovieNet summaries from IMDB. <br>
3- We scrapped the transcripts for all the TVQA videos. <br>

If you're using VLV-Bench in your research or applications, please cite using this BibTeX:
```
<!-- @article{ataallah2024minigpt4,
title={MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens},
author={Ataallah, Kirolos and Shen, Xiaoqian and Abdelrahman, Eslam and Sleiman, Essam and Zhu, Deyao and Ding, Jian and Elhoseiny, Mohamed},
journal={arXiv preprint arXiv:2404.03413},
year={2024}
} -->
```

## Acknowledgements
[Video-ChatGPT](https://mbzuai-oryx.github.io/Video-ChatGPT)

## License
This repository is under [BSD 3-Clause License](LICENSE.md).

原始信息汇总

VLV-Bench: 非常长视频理解的综合基准

数据集下载

• TVQA视频
• MovieNet数据

标注文件

提供了9个技能的标注文件。

基准创建方法

数据抓取

1. 从IMDB抓取所有TVQA的摘要。
2. 从IMDB抓取所有MovieNet的摘要。
3. 抓取所有TVQA视频的转录文本。

引用

如果您在研究或应用中使用VLV-Bench，请使用以下BibTeX引用：

@article{ataallah2024minigpt4, title={MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens}, author={Ataallah, Kirolos and Shen, Xiaoqian and Abdelrahman, Eslam and Sleiman, Essam and Zhu, Deyao and Ding, Jian and Elhoseiny, Mohamed}, journal={arXiv preprint arXiv:2404.03413}, year={2024} }