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--- |
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license: cc-by-nc-sa-4.0 |
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--- |
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# FVQ-20K Dataset |
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## Overview |
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- FVQ-20K is an in-the-wild face video quality assessment (FVQA) dataset, which contains 20,000 face videos with MOS annotations. |
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<br> |
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- The FVQ-20K dataset is divided into training, validation, and test sets with a ratio of 80% : 5% : 15%. |
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## Data Structure |
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``` |
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FVQ-20K |
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│ |
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├── train |
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│ ├── labels.txt |
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│ └── videos |
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│ ├── *.mp4 |
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│ └── ... |
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├── val |
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│ ├── labels.txt |
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│ └── videos |
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│ ├── *.mp4 |
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│ └── ... |
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└── test |
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├── labels.txt |
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└── videos |
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├── *.mp4 |
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└── ... |
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• labels.txt contains video names and their corresponding MOS scores (ranging from 0 to 100). |
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``` |
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## Citation |
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If you use this dataset, please consider citing |
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``` |
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@inproceedings{wu2025fvq, |
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title={FVQ: A Large-Scale Dataset and an LMM-based Method for Face Video Quality Assessment}, |
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author={Wu, Sijing and Li, Yunhao and Xu, Ziwen and Gao, Yixuan and Duan, Huiyu and Sun, Wei and Zhai, Guangtao}, |
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booktitle={Proceedings of the 33rd ACM International Conference on Multimedia}, |
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pages={6928--6937}, |
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year={2025} |
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} |
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``` |
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## Contact |
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- Sijing Wu [(wusijing@sjtu.edu.cn)](wusijing@sjtu.edu.cn) |
