---
title: LVDR
canonical_url: "https://www.modelscope.cn/models/pai/LVDR"
md_url: "https://www.modelscope.cn/models/pai/LVDR.md"
repository: pai/LVDR
last_updated: 2025-04-08
license: cc-by-nc-sa-4.0
library_name:
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 513
stars: 0
---

# LVDR

> LVDR - pai 在 ModelScope 开源的模型。The LVDR Benchmark (Long Video Description Ranking)

pai/LVDR 是 ModelScope 魔搭社区上的机器学习模型，采用 cc-by-nc-sa-4.0 许可。

- **Repository**: pai/LVDR
- **License**: cc-by-nc-sa-4.0
- **Downloads**: 513
- **Stars**: 0
- **Last updated**: 2025-04-08

Source: https://www.modelscope.cn/models/pai/LVDR

---

# The LVDR Benchmark (Long Video Description Ranking)

This benchmark is proposed from [VideoCLIP-XL](https://arxiv.org/abs/2410.00741). 
Given each video and its corresponding ground-truth description, we perform a synthesis process that iterates p − 1 times and alters q words as hallucination during each iteration, resulting in totally p descriptions with gradually increasing degrees of hallucination. We denote such a subset as p × q and construct five subsets as {4 × 1, 4 × 2, 4 × 3, 4 × 4, 4 × 5}. The video CLIP models need to be able to correctly rank these descriptions in descending order of similarity given the video.

# Format
```json 
{
  "long_captions": [
        "...",
    ],
  "video_id": "..."
}
{
  .....
},
.....
```


# Source
~~~
@misc{wang2024videoclipxladvancinglongdescription,
      title={VideoCLIP-XL: Advancing Long Description Understanding for Video CLIP Models}, 
      author={Jiapeng Wang and Chengyu Wang and Kunzhe Huang and Jun Huang and Lianwen Jin},
      year={2024},
      eprint={2410.00741},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.00741}, 
}
~~~