This is the official GitHub page for the paper:
Junaid Ahmed Ghauri, Sherzod Hakimov, and Ralph Ewerth: "Supervised Video Summarization via Multiple Feature Sets with Parallel Attention". In: *In the Proceedings of IEEE International Conference on Multimedia and Expo (ICME) 2021.
The paper is available on:
MSVA is a deep learning model for supervised video summarization. In this research, we address this research gap and investigate how different feature types, i.e., static and motion features, can be integrated in a model architecture for video summarization.
Python version >= 3.6
# clone the repository
git clone [email protected]:VideoAnalysis/MSVA.git
cd MSVA
conda create -n msva python=3.6
conda activate msva
pip install -r requirements.txtExtracted features for the datasets can be downloaded as,
wget -O datasets.tar https://zenodo.org/record/4682137/files/msva_video_summarization.tar
tar -xvf datasets.tarDataset directory details to unerstand files heirarchies:
/datasets
/kinetic_features (features extracted using pretrained I3D model)
/summe
/FLOW
/features
/*.npy (files containing extracted features)
/RGB
/features
/*.npy
/targets (labels synchronized with /object_features labels)
/*.npy
/tvsum
/FLOW
/features
/*.npy
/RGB
/features
/*.npy
/targets
/*.npy
/object_features
/eccv16_dataset_summe_google_pool5.h5
/eccv16_dataset_tvsum_google_pool5.h5
/readme.txt
h5 files structure (object_features)
/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 posotions 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
# for training, crossfold validation according to default parameters "parameters.json".
python train.py -params parameters.jsonpython inference.py -dataset "summe" -video_name "Air_Force_One" -model_weight "model_weights/summe_random_non_overlap_0.5359.tar.pth"
Update parameters.json for desired experimental parameters.s.
{"verbose":False,
"train":True,
"use_cuda":True,
"cuda_device":0,
"max_summary_length":0.15,
"weight_decay":0.00001,
"lr":[0.00005],
"epochs_max":300,
"train_batch_size":5,
"fusion_technique":'inter',
"method":'mean',
"sample_technique":'sub',
"stack":'v',
"name_anchor":"inter_add_aperture_250",
"output_dir" : "./results/",
"apertures":[250],
"combis":[[1,1,1]],
"feat_input":[1024],
"object_features":['datasets/object_features/eccv16_dataset_summe_google_pool5.h5',
'datasets/object_features/eccv16_dataset_tvsum_google_pool5.h5'],
"kinetic_features":"./datasets/kinetic_features/",
"splits":['splits/tvsum_splits.json',
'splits/summe_splits.json',
'splits/summe_random_non_overlap_splits.json',
'splits/tvsum_random_non_overlap_splits.json'],
"feat_input":{"feature_size":365,"L1_out":365,"L2_out":365,"L3_out":512,"pred_out":1,"apperture":250,"dropout1":0.5,"att_dropout1":0.5,"feature_size_1_3":1024,"feature_size_4":365}}"verbose" // True or False : if you want to see detailed running logs or not
"use_cuda" // True or False : if code should execute with GPU or CPU
"cuda_device" // 0, GPU index which will be running the deeplearning code.
"max_summary_length" // 0.15 is the summary length default set in experiments in early work for bench mark dataset
"weight_decay" // 0.00001 weight decay in torch adam optimizer
"lr" // [0.00005] as learning rate during optimization
"epochs_max" // maximum number of epochs in training
"train_batch_size"// 5, the trainign batch size, you can vary this to experiment with.
"fusion_technique" // fusion technique cabe be 'early', 'inter' or 'late'
"method" // this method reffers to early fusion operation. It can be 'min' for minimum, 'max' for maximum or 'mean' to take average of all.
"sample_technique" // this can be 'sub' for sub sample or 'up' for up sample as interpolation when features are not matching the shape
"stack" // in early fusion you want 'v' for vertical stack or 'h' for horizontal stack of features of all sources
"name_anchor" // this is just a name you want to add in to the models name and result files saved during train or test like "inter_add_aperture_250"
"output_dir" // output directory where you want to save the results like "./results/"
"apertures" // aperture size you want to experiment with like [250] but it can be a list you want to treat as hyperparameter optimization like [50, 100, 150, 200, 250, 300, 350, 400]
"combis" // combination of features you want to experiment with for example [[1,1,1]] means all three sources but it can be list of combination to see different combination roles like [[1,0,0],[1,1,1],[1,0,1],[1,1,0],[0,1,1],[0,1,0],[0,0,1]]@article{ghauri2021MSVA,
title={SUPERVISED VIDEO SUMMARIZATION VIA MULTIPLE FEATURE SETS WITH PARALLEL ATTENTION},
author={Ghauri, Junaid Ahmed and Hakimov, Sherzod and Ewerth, Ralph},
Conference={IEEE International Conference on Multimedia and Expo (ICME)},
year={2021}
}
For orignal source of these datasets including videos, Follow: “SumMe, Creating Summaries from User Videos, ECCV 2014” “TVSum , TVSum: Summarizing web videos using titles, 2015”