task_categories:feature-extraction size_categories:10M<n<100M language:en license:cc-by-nc-sa-4.0 arxiv:2307.06942 region:us
The InternVid dataset contains over 7 million videos lasting nearly 760K hours, yielding 234M video clips accompanied by detailed descriptions of total 4.1B words. Our core contribution is to develop a scalable approach to autonomously build a high-quality video-text dataset with large language models (LLM), thereby showcasing its efficacy in learning video-language representation at scale.
@article{wang2023internvid,
  title={InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation},
  author={Wang, Yi and He, Yinan and Li, Yizhuo and Li, Kunchang and Yu, Jiashuo and Ma, Xin and Chen, Xinyuan and Wang, Yaohui and Luo, Ping and Liu, Ziwei and Wang, Yali and Wang, Limin and Qiao, Yu},
  journal={arXiv preprint arXiv:2307.06942},
  year={2023}
}
---
license: cc-by-nc-sa-4.0
task_categories:
- feature-extraction
language:
- en
size_categories:
- 10M<n<100M
---


# InternVid

## Dataset Description

- **Homepage:** [InternVid](https://github.com/OpenGVLab/InternVideo/tree/main/Data/InternVid)
- **Repository:** [OpenGVLab](https://github.com/OpenGVLab/InternVideo/tree/main/Data/InternVid)
- **Paper:** [2307.06942](https://arxiv.org/pdf/2307.06942.pdf)
- **Point of Contact:** mailto:[InternVideo](gvx-sh@pjlab.org.cn)

## InternVid-10M-FLT

We present InternVid-10M-FLT, a subset of this dataset, consisting of 10 million video clips, with generated high-quality captions for publicly available web videos.

## Download

The 10M samples are provided in jsonlines file. Columns include the videoID, timestamps, generated caption and their UMT similarity scores.\

## How to Use

```
from datasets import load_dataset
dataset = load_dataset("OpenGVLab/InternVid")
```


## Method

![Caption Method](assert/caption_fig.jpg)

## Citation

If you find this work useful for your research, please consider citing InternVid. Your acknowledgement would greatly help us in continuing to contribute resources to the research community.

```
@article{wang2023internvid,
  title={InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation},
  author={Wang, Yi and He, Yinan and Li, Yizhuo and Li, Kunchang and Yu, Jiashuo and Ma, Xin and Chen, Xinyuan and Wang, Yaohui and Luo, Ping and Liu, Ziwei and Wang, Yali and Wang, Limin and Qiao, Yu},
  journal={arXiv preprint arXiv:2307.06942},
  year={2023}
}

@article{wang2022internvideo,
  title={InternVideo: General Video Foundation Models via Generative and Discriminative Learning},
  author={Wang, Yi and Li, Kunchang and Li, Yizhuo and He, Yinan and Huang, Bingkun and Zhao, Zhiyu and Zhang, Hongjie and Xu, Jilan and Liu, Yi and Wang, Zun and Xing, Sen and Chen, Guo and Pan, Junting and Yu, Jiashuo and Wang, Yali and Wang, Limin and Qiao, Yu},
  journal={arXiv preprint arXiv:2212.03191},
  year={2022}
}
```