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:
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}
}