This is our implementation of MMDQEN associated with the following paper:
Affective video content analysis via multimodal deep quality embedding network,
Yaochen Zhu, Zhenzhong Chen, Feng Wu
Accepted as a journal paper in IEEE Trans. Affect. Compute, 2020.
The codes are written in Python 3.6.5 with the following packages.
- numpy == 1.16.3
- tensorflow-gpu == 1.13.1
- tensorflow-probability == 0.6.0
We are still applying for permission to release the collected stratified and cleaned version of LIRIS-ACCEDE dataset.
For the original LIRIS-ACCEDE dataset, please visit this URL.
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Extract the multimodal feature as described in the paper:
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Train the MMDQEN model via:
python train.py --affect {val, aro}
For more advanced arguments, run the code with --help argument.
@inproceedings{zhu2019multimodal,
title={Multimodal deep denoise framework for affective video content analysis},
author={Zhu, Yaochen and Chen, Zhenzhong and Wu, Feng},
booktitle={Proceedings of the 27th ACM International Conference on Multimedia},
pages={130--138},
year={2019}
}
@article{zhu2020affective,
title={Affective video content analysis via multimodal deep quality embedding network},
author={Zhu, Yaochen and Chen, Zhenzhong and Wu, Feng},
journal={IEEE Transactions on Affective Computing},
year={2020},
publisher={IEEE}
}