@inproceedings{a739a62281904a5bbfccc9a16e5043ba,
title = "Video frame interpolation based on multi-scale convolutional network and adversarial training",
abstract = "We propose a deep multi-scale convolutional neural network solution for video frame interpolation, which can synthesize the interpolated frames with favorable quality and visual experience. To get sharp results, we use a combination of loss function, including a Wasserstein generative adversarial network loss with gradient penalty. We try a slim generator network structure in order to meet the real-time interpolation requirement as much as possible. In this way our framework contains less parameters, which could be beneficial to video processing tasks in future works. Our work is also shown to be effective in improving subjective visual experience for video frames in most cases.",
keywords = "Adversarial training, Deep learning, Frame synthesis, Multi-scale, Video frame interpolation",
author = "Chenguang Li and Donghao Gu and Xueyan Ma and Kai Yang and Shaohui Liu and Feng Jiang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 3rd IEEE International Conference on Data Science in Cyberspace, DSC 2018 ; Conference date: 18-06-2018 Through 21-06-2018",
year = "2018",
month = jul,
day = "16",
doi = "10.1109/DSC.2018.00089",
language = "英语",
series = "Proceedings - 2018 IEEE 3rd International Conference on Data Science in Cyberspace, DSC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "553--560",
booktitle = "Proceedings - 2018 IEEE 3rd International Conference on Data Science in Cyberspace, DSC 2018",
address = "美国",
}