Wh2018 / hcrn-videoqa

Implementation for the paper "Hierarchical Conditional Relation Networks for Video Question Answering" (Le et al., CVPR 2020, Oral)

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Hierarchical Conditional Relation Networks for Video Question Answering (HCRN-VideoQA)

We introduce a general-purpose reusable neural unit called Conditional Relation Network (CRN) that encapsulates and transforms an array of tensorial objects into a new array of the same kind, conditioned on a contextual feature. The flexibility of CRN units is then examined in solving Video Question Answering, a challenging problem requiring joint comprehension of video content and natural language processing.

Illustrations of CRN unit and the result of model building HCNR for VideoQA:

CRN Unit HCRN Architecture

Check out our paper for details.

Setups

  1. Clone the repository:
 git clone https://github.com/thaolmk54/hcrn-videoqa.git
  1. Download TGIF-QA, MSRVTT-QA, MSVD-QA dataset and edit absolute paths in preprocess/preprocess_features.py and preprocess/preprocess_questions.py upon where you locate your data. Default paths are with /ceph-g/lethao/datasets/{dataset_name}/.

  2. Install dependencies:

conda create -n hcrn_videoqa python=3.6
conda activate hcrn_videoqa
conda install -c conda-forge ffmpeg
conda install -c conda-forge scikit-video
pip install -r requirements.txt

Experiments with TGIF-QA

Depending on the task to chose question_type out of 4 options: action, transition, count, frameqa.

Preprocessing visual features

  1. To extract appearance feature:
python preprocess/preprocess_features.py --gpu_id 2 --dataset tgif-qa --model resnet101 --question_type {question_type}
  1. To extract motion feature:

    Download ResNeXt-101 pretrained model (resnext-101-kinetics.pth) and place it to data/preprocess/pretrained/.

python preprocess/preprocess_features.py --dataset tgif-qa --model resnext101 --image_height 112 --image_width 112 --question_type {question_type}

Note: Extracting visual feature takes a long time. You can download our pre-extracted features from here and save them in data/tgif-qa/{question_type}/. Please use the following command to join split files:

cat tgif-qa_{question_type}_appearance_feat.h5.part* > tgif-qa_{question_type}_appearance_feat.h5

Proprocess linguistic features

  1. Download glove pretrained 300d word vectors to data/glove/ and process it into a pickle file:
python txt2pickle.py
  1. Preprocess train/val/test questions:
python preprocess/preprocess_questions.py --dataset tgif-qa --question_type {question_type} --glove_pt data/glove/glove.840.300d.pkl --mode train
    
python preprocess/preprocess_questions.py --dataset tgif-qa --question_type {question_type} --mode test

Training

Choose a suitable config file in configs/{task}.yml for one of 4 tasks: action, transition, count, frameqa to train the model. For example, to train with action task, run the following command:

python train.py --cfg configs/tgif_qa_action.yml

Evaluation

To evaluate the trained model, run the following:

python validate.py --cfg configs/tgif_qa_action.yml

Note: Pretrained model for action task is available here. Save the file in results/expTGIF-QAAction/ckpt/ for evaluation.

Experiments with MSRVTT-QA and MSVD-QA

The following is to run experiments with MSRVTT-QA dataset, replace msrvtt-qa with msvd-qa to run with MSVD-QA dataset.

Preprocessing visual features

  1. To extract appearance feature:
python preprocess/preprocess_features.py --gpu_id 2 --dataset msrvtt-qa --model resnet101
  1. To extract motion feature:
python preprocess/preprocess_features.py --dataset msrvtt-qa --model resnext101 --image_height 112 --image_width 112

Proprocess linguistic features

Preprocess train/val/test questions:

python preprocess/preprocess_questions.py --dataset msrvtt-qa --glove_pt data/glove/glove.840.300d.pkl --mode train
    
python preprocess/preprocess_questions.py --dataset msrvtt-qa --question_type {question_type} --mode val
    
python preprocess/preprocess_questions.py --dataset msrvtt-qa --question_type {question_type} --mode test

Training

python train.py --cfg configs/msrvtt_qa.yml

Evaluation

To evaluate the trained model, run the following:

python validate.py --cfg configs/msrvtt_qa.yml

Citations

If you make use of this repository for your research, please cite the following paper:

@article{le2020hierarchical,
  title={Hierarchical Conditional Relation Networks for Video Question Answering},
  author={Le, Thao Minh and Le, Vuong and Venkatesh, Svetha and Tran, Truyen},
  journal={arXiv preprint arXiv:2002.10698},
  year={2020}
}

Acknowledgement

  • As for motion feature extraction, we adapt ResNeXt-101 model from this repo to our code. Thank @kenshohara for releasing the code and the pretrained models.
  • We refer to this repo for preprocessing.
  • Our implementation of dataloader is based on this repo.
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Implementation for the paper "Hierarchical Conditional Relation Networks for Video Question Answering" (Le et al., CVPR 2020, Oral)

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