Self and Difference Attention Network for Video Summarization
Preprocessed datasets TVSum, SumMe, YouTube and OVP as well as VASNet pretrained models you can download by running the following command:
./download.sh datasets_models_urls.txt
Datasets will be stored in ./datasets directory and models, with corresponding split files, in ./data/models and ./data/splits respectively.
Original version of the datasets can be downloaded from http://www.eecs.qmul.ac.uk/~kz303/vsumm-reinforce/datasets.tar.gz or https://www.dropbox.com/s/ynl4jsa2mxohs16/data.zip?dl=0.
To train the SADNet on all split files in the ./splits directory run this command:
python3 main.py --train
Results, including a copy of the split and python files, will be stored in ./data directory. You can specify different directory with a parameter -o <directory_name>.
The final results will be recorded in ./data/results.txt with corresponding models in the ./data/models directory.
By default, the training is done with split files in ./splits directory.
I would like to thank to K. Zhou et al. and K Zhang et al. for making the preprocessed datasets publicly available and also Jiri Fajtl et al. for the most of the VASNet code which I copied from https://github.com/ok1zjf/VASNet and modified according to the new Network architecture.
@misc{fajtl2018summarizing,
title={Summarizing Videos with Attention},
author={Jiri Fajtl and Hajar Sadeghi Sokeh and Vasileios Argyriou and Dorothy Monekosso and Paolo Remagnino},
year={2018},
eprint={1812.01969},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@article{DBLP:journals/corr/abs-1811-09791,
author = {Yunjae Jung and Donghyeon Cho and Dahun Kim and Sanghyun Woo and In So Kweon},
title = {Discriminative Feature Learning for Unsupervised Video Summarization},
volume = {abs/1811.09791},
year = {2018},
url = {http://arxiv.org/abs/1811.09791},
archivePrefix = {arXiv},
eprint = {1811.09791},
}