StarVQA: Space-Time Attention for Video Quality Assessment
First, create a conda virtual environment and activate it:
conda create -n StarVQA python=3.7 -y
source activate StarVQA
Then, install the following packages:
- fvcore: pip install 'git+https://github.com/facebookresearch/fvcore'
- simplejson: pip install simplejson
- einops: pip install einops
- timm: pip install timm
- PyAV: conda install av -c conda-forge
- psutil: pip install psutil
- scikit-learn: pip install scikit-learn
- OpenCV: pip install opencv-python
- tensorboard: pip install tensorboard
Clone this repo.
git clone https://github.com/GZHU-DVL/StarVQA.git
cd StarVQA
python setup.py build develop
Please replace the data path with your local path
checkpoint-baidu 提取码:87st
If you find StarVQA useful in your research, please use the following BibTeX entry for citation.
Citation: @article{StarVQA2021,
author={Fengchuang Xing, Yuan-Gen Wang, Hanpin Wang, Leida Li, and Guopu Zhu},
title = {{StarVQA}: Space-Time Attention for Video Quality Assessment},
booktitle = {arXiv preprint arXiv:2108.09635},
pages = {1-5},
year = {2021},
}
StarVQA is built on top of TimeSformer and pytorch-image-models by Ross Wightman. We thank the authors for releasing their code. If you use our model, please consider citing these works as well:
@inproceedings{gberta_2021_ICML,
author = {Gedas Bertasius and Heng Wang and Lorenzo Torresani},
title = {Is Space-Time Attention All You Need for Video Understanding?},
booktitle = {Proceedings of the International Conference on Machine Learning (ICML)},
month = {July},
year = {2021}
}
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
}
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