This repo contains the data and PyTorch code that accompanies our LREC 2020 paper:
LifeQA: A Real-life Dataset for Video Question Answering
Santiago Castro, Mahmoud Azab, Jonathan C. Stroud, Cristina Noujaim, Ruoyao Wang, Jia Deng, Rada Mihalcea
You can see more information at the LifeQA website.
To run it, setup a new environment with Conda and activate it:
conda env create -f environment.yml
conda activate lifeqaThe dataset is under data/, in lqa_train.json, lqa_dev.json,
and lqa_test.json. Even though it's divided in train/dev/test, for most experiments we merge
them and use a five-fold cross-validation, with the folds indicated in data/folds.
You can download the already extracted features or do the following to extract them yourself.
-
Download the videos. Due to YouTube's Term of Service, we can't provide the video files, however we provide the IDs and timestamps to obtain the same data. Download the YouTube videos indicated in the field
parent_video_idfrom the JSON files, cut them based on the fieldsstart_timeandend_time, and save them based on the JSON key (e.g.,213) todata/videos, placing the files there without subdirectories. -
Run
save_frames.shto extract the frames in the video files:bash feature_extraction/save_frames.sh
-
Download pretrained weights from Sports1M for C3D and save it in
data/features/c3d.pickle. -
To extract the features (e.g. from an ImageNet-pretrained ResNet-152) and save them in big H5 files:
mkdir data/features python feature_extraction/extract_features.py resnet
Check the scripts under run_scripts to run the available baselines.
To run TVQA baseline, it's different from the rest of the baselines.
We copied TVQA's repo content from commit 2c98044
into the TVQA/ folder.
It has been changed to support 4 answer choices instead of 5. Some other minor modifications have been done as well.
-
Convert LifeQA dataset to TVQA format
python scripts/to_tvqa_format.py
-
Enter
TVQA/directory:cd TVQA/ -
Setup the interpreter:
conda env create -f environment.yml conda activate tvqa
-
Do some pre-processing:
python preprocessing.py --data_dir ../data/tvqa_format for i in 0 1 2 3 4; do python preprocessing.py --data_dir ../data/tvqa_format/fold${i} done mkdir cache_lifeqa python tvqa_dataset.py \ --input_streams sub \ --no_ts \ --vcpt_path ../data/tvqa_format/det_visual_concepts_hq.pickle \ --train_path ../data/tvqa_format/lqa_train_processed.json \ --valid_path ../data/tvqa_format/lqa_dev_processed.json \ --test_path ../data/tvqa_format/lqa_test_processed.json \ --word2idx_path cache_lifeqa/word2idx.pickle \ --idx2word_path cache_lifeqa/idx2word.pickle \ --vocab_embedding_path cache_lifeqa/vocab_embedding.pickle
For 5-fold cross-validation:
for i in 0 1 2 3 4; do
python main.py \
--input_streams sub vcpt \
--no_ts \
--vcpt_path ../data/tvqa_format/det_visual_concepts_hq.pickle \
--train_path ../data/tvqa_format/fold${i}/train_processed.json \
--valid_path ../data/tvqa_format/fold${i}/validation_processed.json \
--test_path ../data/tvqa_format/fold${i}/test_processed.json \
--word2idx_path cache_lifeqa/word2idx.pickle \
--idx2word_path cache_lifeqa/idx2word.pickle \
--vocab_embedding_path cache_lifeqa/vocab_embedding.pickle
python test.py --model_dir $(ls -t results/ | head -1) --mode test
doneFor train, dev and test partitions:
python main.py \
--input_streams sub vcpt \
--no_ts \
--vcpt_path ../data/tvqa_format/det_visual_concepts_hq.pickle \
--train_path ../data/tvqa_format/lqa_train_processed.json \
--valid_path ../data/tvqa_format/lqa_dev_processed.json \
--test_path ../data/tvqa_format/lqa_test_processed.json \
--word2idx_path cache_lifeqa/word2idx.pickle \
--idx2word_path cache_lifeqa/idx2word.pickle \
--vocab_embedding_path cache_lifeqa/vocab_embedding.pickle
python test.py --model_dir $(ls -t results/ | head -1) --mode testpython preprocessing.py
mkdir cache_original
python tvqa_dataset.py \
--input_streams sub \
--no_ts \
--word2idx_path cache_original/word2idx.pickle \
--idx2word_path cache_original/idx2word.pickle \
--vocab_embedding_path cache_lifeqa/vocab_embedding.pickle
python main.py \
--input_streams sub vcpt \
--no_ts
RESULTS_FOLDER_NAME=$(ls -t results/ | head -1)The result from this part was saved in results_2019_05_16_23_02_15 in Google Drive. Note it corresponds to S+V+Q, with cpt as the video feature and w/o ts.
For 5-fold cross-validation:
for i in 0 1 2 3 4; do
python test.py \
--vcpt_path ../data/tvqa_format/det_visual_concepts_hq.pickle \
--test_path ../data/tvqa_format/fold${i}/test_processed.json \
--model_dir "${RESULTS_FOLDER_NAME}" \
--mode test
doneFor the test partition:
python test.py \
--vcpt_path ../data/tvqa_format/det_visual_concepts_hq.pickle \
--test_path ../data/tvqa_format/lqa_test_processed.json \
--model_dir "${RESULTS_FOLDER_NAME}" \
--mode testFor 5-fold cross-validation:
python main.py \
--input_streams sub vcpt \
--no_ts \
--vcpt_path ../data/tvqa_format/det_visual_concepts_hq.pickle \
--train_path ../data/tvqa_format/fold${i}/train_processed.json \
--valid_path ../data/tvqa_format/fold${i}/validation_processed.json \
--test_path ../data/tvqa_format/fold${i}/test_processed.json \
--word2idx_path cache_original/word2idx.pickle \
--idx2word_path cache_original/idx2word.pickle \
--vocab_embedding_path cache_original/vocab_embedding.pickle \
--pretrained_model_dir "${RESULTS_FOLDER_NAME}" \
--new_word2idx_path cache_lifeqa/word2idx.pickleFor train, dev and test partitions:
python main.py \
--input_streams sub vcpt \
--no_ts \
--vcpt_path ../data/tvqa_format/det_visual_concepts_hq.pickle \
--train_path ../data/tvqa_format/lqa_train_processed.json \
--valid_path ../data/tvqa_format/lqa_dev_processed.json \
--test_path ../data/tvqa_format/lqa_test_processed.json \
--word2idx_path cache_original/word2idx.pickle \
--idx2word_path cache_original/idx2word.pickle \
--vocab_embedding_path cache_original/vocab_embedding.pickle \
--pretrained_model_dir "${RESULTS_FOLDER_NAME}" \
--new_word2idx_path cache_lifeqa/word2idx.pickleIf you encounter issues while using our data or code, please open an issue in this repo.
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