Efficient Video Question Answering

This is the implementation of our paper, all features and weights will be released on github. You can also extract video and text features yourself according to our code and documentation.

Environment

This code is tested with:

• Ubuntu 20.04
• PyTorch >= 1.8
• CUDA >= 10.1

# create your virtual environment
conda create --name effvqa python=3.7
conda activate atp

# dependencies
conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=10.1 -c pytorch
conda install pandas

# optional (for feature extraction); see also tools/*.py
pip install git+/~https://github.com/openai/CLIP.git

Dataset

• Annotation: check ./data/Annotation
• Source data:
  • Next-QA: https://xdshang.github.io/docs/vidor.html
  • MSR-VTT & MSVD: /~https://github.com/xudejing/video-question-answering
  • ActivityNet-QA: /~https://github.com/MILVLG/activitynet-qa
  • TGIF: /~https://github.com/YunseokJANG/tgif-qa

Feature extraction

Please refer to ./tools/extract_video_feature_nextqa.py and ./tools/extract_text_feature_nextqa.py

Train & Val & Test

Check trainval.sh

python3 src/trainval.py --use_text_query --use_text_cands --n_frames 16  --n_cands 5\
        --dataset 'nextqa_mc' --data_path './data/Annotation' --feature_path './path_to_your_Data/NeXt-QA'\
        --batch_size 512 --method 'prob' --tao 0.04

python3 src/test.py --use_text_query --use_text_cands --n_frames 64 --n_cands 5\
        --dataset 'nextqa_mc' --split 'test' --data_path './data/Annotation' --feature_path './path_to_your_Data/NeXt-QA'\
        --checkpoint './checkpoints/nextqa_mc/ckpt_0.5745.pth'\
        --batch_size 512 --method 'prob'

LICENSE / Contact

We release this repo under the open MIT License.

Acknowledgements

We reference the excellent repos of NeXT-QA, ATP, CLIP, in addition to other specific repos to the datasets/baselines we examined (see paper). If you build on this work, please be sure to cite these works/repos as well.