Skip to content
A PyTorch Implementation of SUM-GAN-AAE from "Unsupervised Video Summarization via Attention-Driven Adversarial Learning" (MMM 2020)
Python Shell
Branch: master
Clone or download
Latest commit 5099136 Jan 13, 2020
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
data Create eccv16_dataset_tvsum_google_pool5.rar Nov 1, 2019
evaluation Create knapsack_implementation.py Nov 1, 2019
model Update solver.py Nov 26, 2019
LICENSE.md Create LICENSE.md Nov 1, 2019
README.md Update README.md Jan 13, 2020

README.md

Unsupervised Video Summarization via Attention-Driven Adversarial Learning

PyTorch Implementation of SUM-GAN-AAE

  • From "Unsupervised Video Summarization via Attention-Driven Adversarial Learning" (to appear in the 26th Int. Conference on Multimedia Modeling (MMM 2020), January 5-8, 2020, Daejeon, Korea)
  • Written by Evlampios Apostolidis, Eleni Adamantidou, Alexandros I. Metsai, Vasileios Mezaris and Ioannis Patras
  • This software can be used for training an attention-driven, GAN-based deep learning architecture for automatic video summarization. Training is performed in a fully unsupervised manner without the need for ground-truth data (such as human-generated video summaries). After being unsupervisingly trained on a collection of videos, the SUM-GAN-AAE model is capable of producing representative summaries for unseen videos, according to a user-specified time-budget about the summary duration.

Main dependencies

  • Python 3.6
  • PyTorch 1.0.1

Data

Structured h5 files with the video features and annotations of the SumMe and TVSum datasets are available within the "data" folder. The GoogleNet features of the video frames were extracted by Ke Zhang and Wei-Lun Chao and the h5 files were obtained from Kaiyang Zhou. These files have the following structure:

/key
    /features                 2D-array with shape (n_steps, feature-dimension)
    /gtscore                  1D-array with shape (n_steps), stores ground truth improtance score (used for training, e.g. regression loss)
    /user_summary             2D-array with shape (num_users, n_frames), each row is a binary vector (used for test)
    /change_points            2D-array with shape (num_segments, 2), each row stores indices of a segment
    /n_frame_per_seg          1D-array with shape (num_segments), indicates number of frames in each segment
    /n_frames                 number of frames in original video
    /picks                    positions of subsampled frames in original video
    /n_steps                  number of subsampled frames
    /gtsummary                1D-array with shape (n_steps), ground truth summary provided by user (used for training, e.g. maximum likelihood)
    /video_name (optional)    original video name, only available for SumMe dataset

Original videos and annotations for each dataset are also available in the authors' project webpages:

Training

To train the model using one of the aforementioned datasets and for a number of randomly created splits of the dataset (where in each split 80% of the data is used for training and 20% for testing) use the corresponding JSON file that is included in the "data/splits" directory. This file contains the 5 randomly generated splits that were utilized in our experiments.

For training the model using a single split, run:

python main.py --split_index N (with N being the index of the split)

Alternatively, to train the model for all 5 splits, use the 'run_splits.sh' script according to the following:

chmod +x run_splits.sh    # Makes the script executable.
./run_splits              # Runs the script.  

Please note that after each training epoch the algorithm performs an evaluation step, and uses the trained model to compute the importance scores for the frames of each test video. These scores are then used by the provided evaluation scripts to assess the overal performance of the model (in F-Score).

The progress of the training can be monitored via the TensorBoard platform and by:

  • opening a command line (cmd) and running: tensorboard --logdir=/path/to/log-directory --host=localhost
  • opening a browser and pasting the returned URL from cmd

Configurations

Setup for the training process:

  • On 'data_loader.py', specify the path to the 'h5' file of the dataset and the path to the 'json' file containing data about the created splits.
  • On 'configs.py', define the directory where the models will be saved to.

Arguments in 'configs.py':

--video_type: The used dataset for training the model. Can be either 'TVSum' or 'SumMe'.
--input_size: The size of the input feature vectors (1024 for GoogLeNet features).
--hidden_size: The hidden size of the LSTM units.
--num_layers: The number of layers of each LSTM network.
--regularization_factor: The value of the reguralization factor (ranges from 0.0 to 1.0).
--n_epochs: Number of training epochs.
--clip: The gradient clipping parameter.
--lr: Learning rate.
--discriminator_lr: Discriminator's learning rate.
--split_index: The index of the current split.

For the parameters with no explicitly defined default values, please read the paper ("Implementation Details" section) or check the 'configs.py' file.

Evaluation

Using multiple human-generated summaries per video: This is the typical evaluation protocol used in the literature. To evaluate the models by comparing, after each training epoch, the generated summary for each test video (based on the computed importance scores) against a set of multiple human summaries that are available for that video (see the '/user_summary' entry in the explanation of the h5 file structure in the Data section above), run the 'check_fscores_summe.py' and 'check_fscores_tvsum.py' scripts, after specifying:

a) the path to the folder where the json files with the analysis results (i.e. frame-level importance scores) are stored
b) the path to the h5 files of the datasets.

Using a single ground-truth summary (compiled from multiple human inputs) per video: This is an alternate evaluation protocol used in the literature. To evaluate the models by comparing the generated summary for each test video (based on the computed importance scores) against a single groung-truth summary that is available for that video (see the '/gtscore' entry in the explanation of the h5 file structure in the Data section above), run the 'check_fscores_summe_with_gts.py' and 'check_fscores_tvsum_with_gts.py' scripts, after specifying:

a) the path to the folder where the json files with the analysis results (i.e. frame-level importance scores) are stored
b) the path to the h5 files of the datasets.

Citation

If you find this code useful in your work, please cite the following publication:

E. Apostolidis, E. Adamantidou, A. I. Metsai, V. Mezaris, I. Patras. "Unsupervised Video Summarization via Attention-Driven Adversarial Learning". Proc. 26th Int. Conference on Multimedia Modeling (MMM 2020), January 5-8, 2020, Daejeon, Korea

DOI: https://doi.org/10.1007/978-3-030-37731-1_40

License

Copyright (c) 2019, Evlampios Apostolidis, Eleni Adamantidou, Alexandros I. Metsai, Vasileios Mezaris, Ioannis Patras / CERTH-ITI. All rights reserved. This code is provided for academic, non-commercial use only. Redistribution and use in source and binary forms, with or without modification, are permitted for academic non-commercial use provided that the following conditions are met:

  1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
  2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation provided with the distribution.

This software is provided by the authors "as is" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the authors be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage.

Acknowledgement

This work was supported by the European Union Horizon 2020 research and innovation programme under contract H2020-780656 ReTV. The work of Ioannis Patras has been supported by EPSRC under grant No. EP/R026424/1.

You can’t perform that action at this time.