• From "Combining Global and Local Attention with Positional Encoding for Video Summarization", Proc. of the IEEE Int. Symposium on Multimedia (ISM), Dec. 2021.
• Written by Evlampios Apostolidis, Georgios Balaouras, Vasileios Mezaris and Ioannis Patras.
• This software can be used for training a deep learning architecture which estimates frames' importance after modeling their dependencies with the help of global and local multi-head attention mechanisms that integrate a positional encoding component. Training is performed in a supervised manner based on ground-truth data (human-generated video summaries). After being trained on a collection of videos, the PGL-SUM model is capable of producing representative summaries for unseen videos, according to a user-specified time-budget about the summary duration.

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 importance 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 dataset providers' webpages:

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 model/main.py --split_index N --n_epochs E --batch_size B --video_type 'dataset_name'

where, N refers to the index of the used data split, E refers to the number of training epochs, B refers to the batch size, and dataset_name refers to the name of the used dataset.

Alternatively, to train the model for all 5 splits, use the run_summe_splits.sh and/or run_tvsum_splits.sh script and do the following:

chmod +x model/run_summe_splits.sh    # Makes the script executable.
chmod +x model/run_tvsum_splits.sh    # Makes the script executable.
./model/run_summe_splits.sh           # Runs the script. 
./model/run_tvsum_splits.sh           # Runs the script.  

Please note that after each training epoch the algorithm performs an evaluation step, using the trained model to compute the importance scores for the frames of each video of the test set. These scores are then used by the provided evaluation scripts to assess the overall 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.

Setup for the training process:

• In data_loader.py, specify the path to the h5 file of the used dataset, and the path to the JSON file containing data about the utilized data splits.
• In configs.py, define the directory where the analysis results will be saved to.

The utilized model selection criterion relies on the post-processing of the calculated losses over the training epochs and enables the selection of a well-trained model by indicating the training epoch. To evaluate the trained models of the architecture and automatically select a well-trained model, define the dataset_path in compute_fscores.py and run evaluate_exp.sh. To run this file, specify:

• base_path/exp$exp_num: the path to the folder where the analysis results are stored,
• $dataset: the dataset being used, and
• $eval_method: the used approach for computing the overall F-Score after comparing the generated summary with all the available user summaries (i.e., 'max' for SumMe and 'avg' for TVSum).

sh evaluation/evaluate_exp.sh $exp_num $dataset $eval_method

For further details about the adopted structure of directories in our implementation, please check line #6 and line #11 of evaluate_exp.sh.

We have released the trained models for our main experiments -namely Table III and Table IV- of our ISM 2021 paper. The inference.py script, lets you evaluate the -reported- trained models, for our 5 randomly-created data splits. Firstly, download the trained models, with the following script:

sudo apt-get install unzip wget
wget "https://zenodo.org/record/5635735/files/pretrained_models.zip?download=1" -O pretrained_models.zip
unzip pretrained_models.zip -d inference
rm -f pretrained_models.zip

Then, specify the PATHs for the model, the split_file and the dataset in use. Finally, run the script with the following syntax

python inference/inference.py --table ID --dataset 'dataset_name'

where, ID refers to the id of the reported table, and dataset_name refers to the name of the used dataset.

Given the above pre-trained models, we present some additional evaluation results following the rank-based evaluation protocol proposed here, and the diversity measure described here. Moreover, we provide results with regards to the trainable parameters and the required time for training. The code for implementing the rank-based evaluation protocol is available at CA-SUM, and the summary diversity was measured using the relevant code from DSNet.

If you find our work, code or pretrained models, useful in your work, please cite the following publication:

E. Apostolidis, G. Balaouras, V. Mezaris, I. Patras, "Combining Global and Local Attention with Positional Encoding for Video Summarization", Proc. IEEE Int. Symposium on Multimedia (ISM), Dec. 2021.

Copyright (c) 2021, Evlampios Apostolidis, Georgios Balaouras, 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:

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