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Unsupervised video summarization with deep reinforcement learning (AAAI'18)
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| 2026-08-06 | 504 |
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# pytorch-vsumm-reinforce This repo contains the Pytorch implementation of the AAAI'18 paper - [Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward](https://arxiv.org/abs/1801.00054). The original Theano implementation can be found [here](https://github.com/KaiyangZhou/vsumm-reinforce). <div align="center"> <img src="imgs/pipeline.jpg" alt="train" width="80%"> </div> The main requirements are [pytorch](http://pytorch.org/) (`v0.4.0`) and python `2.7`. Some dependencies that may not be installed in your machine are [tabulate](https://pypi.org/project/tabulate/) and [h5py](https://github.com/h5py/h5py). Please install other missing dependencies. ## Get started 1. Download preprocessed datasets ```bash git clone https://github.com/KaiyangZhou/pytorch-vsumm-reinforce cd pytorch-vsumm-reinforce # download datasets.tar.gz (173.5MB) wget http://www.eecs.qmul.ac.uk/~kz303/vsumm-reinforce/datasets.tar.gz tar -xvzf datasets.tar.gz ``` **Updates**: The QMUL server is inaccessible. Download the datasets from this [google drive link](https://drive.google.com/open?id=1Bf0beMN_ieiM3JpprghaoOwQe9QJIyAN). 2. Make splits ```bash python create_split.py -d datasets/eccv16_dataset_summe_google_pool5.h5 --save-dir datasets --save-name summe_splits --num-splits 5 ``` As a result, the dataset is randomly split for 5 times, which are saved as json file. Train and test codes are written in `main.py`. To see the detailed arguments, please do `python main.py -h`. ## How to train ```bash python main.py -d datasets/eccv16_dataset_summe_google_pool5.h5 -s datasets/summe_splits.json -m summe --gpu 0 --save-dir log/summe-split0 --split-id 0 --verbose ``` ## How to test ```bash python main.py -d datasets/eccv16_dataset_summe_google_pool5.h5 -s datasets/summe_splits.json -m summe --gpu 0 --save-dir log/summe-split0 --split-id 0 --evaluate --resume path_to_your_model.pth.tar --verbose --save-results ``` If argument `--save-results` is enabled, output results will be saved to `results.h5` under the same folder specified by `--save-dir`. To visualize the score-vs-gtscore, simple do ```bash python visualize_results.py -p path_to/result.h5 ``` ## Plot We provide codes to plot the rewards obtained at each epoch. Use `parse_log.py` to plot the average rewards ```bash python parse_log.py -p path_to/log_train.txt ``` The plotted image would look like <div align="center"> <img src="imgs/overall_reward.png" alt="overall_reward" width="50%"> </div> If you wanna plot the epoch-reward curve for some specific videos, do ```bash python parse_json.py -p path_to/rewards.json -i 0 ``` You will obtain images like <div align="center"> <img src="imgs/epoch_reward_0.png" alt="epoch_reward" width="30%"> <img src="imgs/epoch_reward_13.png" alt="epoch_reward" width="30%"> <img src="imgs/epoch_reward_15.png" alt="epoch_reward" width="30%"> </div> If you prefer to visualize the epoch-reward curve for all training videos, try `parse_json.sh`. Modify the code according to your purpose. ## Visualize summary You can use `summary2video.py` to transform the binary `machine_summary` to real summary video. You need to have a directory containing video frames. The code will automatically write summary frames to a video where the frame rate can be controlled. Use the following command to generate a `.mp4` video ```bash python summary2video.py -p path_to/result.h5 -d path_to/video_frames -i 0 --fps 30 --save-dir log --save-name summary.mp4 ``` Please remember to specify the naming format of your video frames on this [line](https://github.com/KaiyangZhou/pytorch-vsumm-reinforce/blob/master/summary2video.py#L22). ## How to use your own data We preprocess data by extracting image features for videos and save them to `h5` file. The file format looks like [this](https://github.com/KaiyangZhou/vsumm-reinforce/issues/1#issuecomment-363492711). After that, you can make split via `create_split.py`. If you wanna train policy netwo
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matched fp:b195313463548976, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning
matched fp:b195313463548976, topic:deep-learning
matched fp:b195313463548976, topic:computer-vision