anshuman23/fair_video_summarization_tmlr

Towards Fair Video Summarization

Accepted to TMLR 2023

Table of Contents

• Environment Setup
• Dataset
• Evaluation
• Training Models and Generating Summaries

Environment Setup

• Python 3.8.16
• CUDA 11.1
• Ubuntu 20.04.03

Anaconda

conda env create -f environment.yml

Dataset

All necessary data files are already provided in the repo. datasets/fvs.h5 contains the FairVidSum dataset. splits/fvs_splits.json is the splits used for our benchmarks. fair_npy_data/ contains all fairness labels and data required for SumBal evaluations and generating summaries using FVS-LP. fair_npy_data/ can also be generated using src/extract_fair_labels.py

To generate fair_npy_data/ yourself, please download faces_fvs_tvsum.csv, place it in src/ and run python extract_fair_labels.py.

Please note the output of extract_fair_labels.py provides the dictionary to translate the int labels (for violating groups output from SumBal) to groups/sex/individuals. Our output is provided in src/fair_labels_info.log

Evaluation

Please ensure fair_npy_data/ is downloaded. All our generated model summaries are already provded in repo in respective models directories (models/). Please follow Training Models and Generating Summaries to train models and generate new model summaries.

Average F1 Scores

To evaluate all models on all splits please run:

cd eval
python eval_fscore.py -m all

SumBals

To evaluate all models on all splits please run:

cd eval
python eval_sumbal_all.py -m all

Training Models and Generating Summaries

All (best) summaries from trained models are stored per split as .pkl files. Our generated summaries are already provided.

To train and generate summaries yourself, please follow the steps below for ever model.

<split_idx> is commonly used as an input to various training, which defines the split to train and generate summaries for and will be in range [0-4] for our 5 splits.

Note: AC-SUM-GAN, CA-SUM, SUM-GAN-AAE, SUM-GAN-SL, PGL-SUM follow same procedure, where train and summaries are generated at same time using a single script for each individual split in similar fashion. Summaries for these models will be stores in models/<model_name>/exp2/fvs/summaires

FVS-LP

cd lp-fvs
python lp-fvs.py

All generated summaries stored in models/lp-fvs/summaries

Random

cd random
python gen_rand_summary.py

All generated summaries stored in models/random/summaries

Human

cd human
python gen_user_summary.py

All generated summaries stored in models/human/summaries

AC-SUM-GAN

cd models/ac-sum-gan
python ac-sum-gan.py --split_index <split_idx>

Please repeat and run for split_index=[0,1,2,3,4] to generate summaries for all splits.

Summaries are stored in models/ac-sum-gan/exp2/fvs/summaires, trained models (as .pth) and training fscore results (as .txt files) will appear in models/ac-sum-gan/exp2/fvs/models and models/ac-sum-gan/exp2/fvs/results respectively. All results are per split and will be in folders in format split<split_idx>/.

SUM-GAN-AAE

Same procedure as AC-SUM-GAN. Run for all split_idx=[0,1,2,3,4]

cd models/sum-gan-aae
python sum-gan-aae.py --split_index <split_idx>

SUM-GAN-SL

Same procedure as AC-SUM-GAN. Run for all split_idx

cd models/sum-gan-sl
python sum-gan-sl.py --split_index <split_idx>

PGL-SUM

Same procedure as AC-SUM-GAN. Run for all split_idx

cd models/pgl-sum
python pgl-sum.py --split_index <split_idx>

CA-SUM

Same procedure as AC-SUM-GAN. Run for all split_idx

cd models/ca-sum
python ca-sum.py --split_index <split_idx>

DSNet

For DSNet, training and generating summaries are seperated. DSNet uses fvs_splits.yml instead of fvs_splits.json. The .yml splits file is already provided in dsnet/splits/

Train DSNet

DSNet trains for all splits. To train:

cd dsnet/src
python train.py anchor-free --model-dir models/fvs/ --splits ../splits/fvs.yml

All saved models (checkpoints) will be in dsnet/src/models/fvs/checkpoint for each split. Saved in format: fvs.yml.<split_idx>.pt for example fvs.yml.0.pt

Generate DSNet summaries

cd models/dsnet/src
python gen_summaries.py --ckpt_path <path_to_chkpt> –-split
<split_idx>

For instance, to generate summaries for Split 0:

python gen_summaries.py --ckpt_path models/fvs/checkpoint/fvs.yml.0.pt –-split 0

Please generate summaries for all 5 splits [0,1,2,3,4]. All generated summaries are stored in dsnet/src/models/fvs/summaires

VASNet

Similar to DSNet, training and generating summaries are seperated and all splits will be trained with one script.

Train VasNet

cd models/vasnet/VASNet
python main.py -r ../VASNet -d ../../../datasets/fvs.h5
 -s ../../../splits/fvs_splits.json -t

All trained models will be asved in vasnet/VASNet/data/models in format fvs_splits_<split_idx>_<best_fscore>.tar.pth

Generate VASNet summaries

cd models/vasnet/VASNet
python gen_summaries.py --model_path <path_to_chkpt> --split <split_idx>

For instance, to generate summaries for Split 0:

python gen_summaries.py --model_path models/fvs/checkpoint/fvs_splits_0_0.6613551421287026.tar.pth –-split 0

Please generate summaries for all 5 splits [0,1,2,3,4]. All generated summaries are stored in vasnet/VASNet/data/summaires

SUM-IND

Similar to AC-GAN-SUM like training procedures, SUM-IND script trains and generates summaries per split.

 python sum_ind/main.py -d ../../datasets/fvs.h5 -s ../../splits/fvs_splits.json -m tvsum --split-id <split_idx> --save-dir sum-ind<split_idx>

Please run for all 5 splits split_idx=[0,1,2,3,4]. Generated summaries are stored in models/sum-ind/summaries.