Official Repository for ACMMM 2023 Paper: "Towards Explainable Video Quality Assessment: a Database and a Language-prompt Approach." Paper Link: Arxiv

Welcome to visit Sibling Repositories from our team:

FAST-VQA

DOVER

Zero-shot BVQI

The database (Maxwell, training part) will be released before end of July. Stay tuned!

The code and pre-trained weights of MaxVQA are released in this repo.

Install and modify OpenCLIP:

git clone https://github.com/mlfoundations/open_clip.git
cd open_clip
sed -i '92s/return x\[0\]/return x/' src/open_clip/modified_resnet.py 
pip install -e .

Install DOVER for Pre-processing and FAST-VQA weights:

git clone https://github.com/vqassessment/DOVER.git
cd DOVER
pip install -e .
mkdir pretrained_weights 
cd pretrained_weights 
wget https://github.com/VQAssessment/DOVER/releases/download/v0.1.0/DOVER.pth 

demo_maxvqa.py

You can maintain a custom service for multi-dimensional VQA.

infer_from_videos.py

infer_from_feats.py

For the first run, the script will extract features from videos.

For the default setting, train on LIVE-VQC, KoNViD-1k, and YouTube-UGC.

train_multi_existing.py -o LKY.yml

You can also modify the yaml file to include more datasets for training.

Will be added soon.

Please feel free to cite our paper:

@article{wu2023maxvqa,
  title={Towards Explainable In-the-Wild Video Quality Assessment: a Database and a Language-Prompted Approach},
  author={Wu, Haoning and Zhang, Erli and Liao, Liang and Chen, Chaofeng and Hou, Jingwen and Wang, Annan  and Sun, Wenxiu and Yan, Qiong and Lin, Weisi},
  journal={Arxiv Preprint},
  year={2023}
}