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A PyTorch implementation of our paper Video summarization with u-shaped transformer. Published in Applied Intelligence.

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Video summarization with u-shaped transformer

A PyTorch implementation of our paper Video summarization with u-shaped transformer. Published in Applied Intelligence.

Getting Started

This project is developed on Ubuntu 16.04 with CUDA 9.0.176.

git clone https://github.com/semchan/Uformer.git

Install python dependencies.

pip install -r requirements.txt

Datasets and pretraining models Preparation

Download the pre-processed datasets into datasets/ folder, including TVSum, SumMe, OVP, and YouTube datasets.

Now the datasets structure should look like

UFormer
└── datasets/
    ├── eccv16_dataset_ovp_google_pool5.h5
    ├── eccv16_dataset_summe_google_pool5.h5
    ├── eccv16_dataset_tvsum_google_pool5.h5
    ├── eccv16_dataset_youtube_google_pool5.h5
    └── readme.txt
└── eval_models/
    ├── ab_ovp_youtube
    ├── anchor_based
        ├──ab_basic
        ├──augmented
        ├──canonical
        └──transfer
    └── anchor_free
        └──canonical

Evaluation

To evaluate your anchor-based models, run

sh evaluate.sh

Training

To train anchor-based attention model on TVSum and SumMe datasets with canonical settings, run

python train.py --model anchor-based --model-dir ./models/ab_basic --splits ./splits/tvsum.yml ./splits/summe.yml

Acknowledgments

We gratefully thank the below open-source repo, which greatly boost our research.

  • Thank Part of the code is referenced from: DSNet. Thanks for their great work before.
  • Thank KTS for the effective shot generation algorithm.
  • Thank DR-DSN for the pre-processed public datasets.
  • Thank VASNet for the training and evaluation pipeline.

Citation

If you find our codes or paper helpful, please consider citing.

@article{chen2022video,
  title={Video summarization with u-shaped transformer},
  author={Chen, Yaosen and Guo, Bing and Shen, Yan and Zhou, Renshuang and Lu, Weichen and Wang, Wei and Wen, Xuming and Suo, Xinhua},
  journal={Applied Intelligence},
  pages={1--17},
  year={2022},
  publisher={Springer}
}

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A PyTorch implementation of our paper Video summarization with u-shaped transformer. Published in Applied Intelligence.

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