VSFA code for the following papers:
- Dingquan Li, Tingting Jiang, and Ming Jiang. Quality Assessment of In-the-Wild Videos. In Proceedings of the 27th ACM International Conference on Multimedia (MM ’19), October 21-25, 2019, Nice, France. [arxiv version]
CUDA_VISIBLE_DEVICES=0 python CNNfeatures.py --database=KoNViD-1k --frame_batch_size=64
You need to specify the database and change the corresponding videos_dir.
CUDA_VISIBLE_DEVICES=0 python VSFA.py --database=KoNViD-1k --exp_id=0
You need to specify the database and exp_id.
tensorboard --logdir=logs --port=6006 # in the server (host:port)
ssh -p port -L 6006:localhost:6006 user@host # in your PC. See the visualization in your PCWe set seeds for the random generators and re-run the experiments on the same ten splits, i.e., the first 10 splits (exp_id=0~9). The results may be still not the same among different version of PyTorch. See randomness@Pytorch Docs
The reproduced overall results are better than the previous results published in the paper. We add learning rate scheduling in the updated code. Better hyper-parameters may be set, if you "look" at the training loss curve and the curves of validation results.
The mean (std) values of the first ten index splits (60%:20%:20% train:val:test)
| KoNViD-1k | CVD2014 | LIVE-Qualcomm | |
|---|---|---|---|
| SROCC | 0.7728 (0.0189) | 0.8698 (0.0368) | 0.7726 (0.0611) |
| KROCC | 0.5784 (0.0194) | 0.6950 (0.0465) | 0.5871 (0.0620) |
| PLCC | 0.7754 (0.0192) | 0.8678 (0.0315) | 0.7954 (0.0553) |
| RMSE | 0.4205 (0.0211) | 10.8572 (1.3518) | 7.5495 (0.7017) |
The model weights provided in models/VSFA.pt are the saved weights when running the 9-th split of KoNViD-1k.
python test_demo.py --video_path=test.mp4
conda create -n reproducibleresearch pip python=3.6
source activate reproducibleresearch
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
source deactive- PyTorch 1.1.0
- TensorboardX 1.2, TensorFlow-TensorBoard
Note: The codes can also be directly run on PyTorch 1.3.
Dingquan Li, dingquanli AT pku DOT edu DOT cn.