video_summary_generaton

This repo contains the Pytorch implementation of the AAAI'18 paper - Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward.

The repo contains 2 files.

1. training.ipynb
2. testing.ipynb

Please assign the path of training data set to the variable 'input_videos_folder' in training.py.The program will preprocess the data and will generate model.

Please assign the path of testing data set to the variable 'input_videos_folder' in testing.py and provide path of the model generated during training.The summary will be generated in a separate folder 'summary_videos'.

For more details contact me: anaghazac@gmail.com

Reference Repo

1. https://github.com/KaiyangZhou/pytorch-vsumm-reinforce
2. https://github.com/TorRient/Video-Summarization-Pytorch

Reference papers

1. Kaiyang Zhou,Yu Qia,Tao Xian.: "Reinforcement Learning for unsupervised video summarization with diversity-representativeness reward", arxiv:1801.00054v3[cs] , Feb.2018.
2. Tianrui Liu, Qingjie Meng, Athanasios Vlontzos, Jeremy Tan, DanielRueckert, Bernhard Kainz.:”Ultrasound Video Summarization usingDeep Reinforcement Learning”, arXiv:2005.09531 [cs], May. 2020.
3. Danila Ptapov,Matthijs Douze, Zaid Harchaouni,Cordelia Schmid.:”Category-specific video summarization. ECCV-European conferenceon computer vision, Sep 2014,Zurich,Switzerland. pp.540-555,10.1007/978-3-319-10599-435.hal-01022967
4. Zhang, K., Chao, W.L., Sha, F., Grauman, K.: Video summarization with longshort-term memory. In: European conference on computer vision. pp. 766–782.Springer (2016)