Awesome

HQGA: Video as Conditional Graph Hierarchy for Multi-Granular Question Answering

Todo

2. Release features of NExT-QA(BERT feature are from NExT-QA)[2021/12/23].

Environment

Anaconda 4.8.4, python 3.6.8, pytorch 1.6 and cuda 10.2. For other libs, please refer to the file requirements.txt.

Install

Please create an env for this project using anaconda (should install anaconda first)

>conda create -n videoqa python==3.6.8
>conda activate videoqa
>git clone https://github.com/doc-doc/HQGA.git
>pip install -r requirements.txt

Data Preparation

We use MSVD-QA as an example to help get farmiliar with the code. Please download the pre-computed features and trained models here

After downloading the data, please create a folder ['data/'] at the same directory as ['HQGA'], then unzip the video and QA features into it. You will have directories like ['data/msvd/' and 'HQGA/'] in your workspace. Please move the model file [.ckpt] into ['HQGA/models/msvd/'].

Usage

Once the data is ready, you can easily run the code. First, to test the environment and code, we provide the prediction and model of the HQGA on MSVD-QA. You can get the results reported in the paper by running:

>python eval_oe.py

The command above will load the prediction file under ['results/msvd/'] and evaluate it. You can also obtain the prediction by running:

>./main.sh 0 test #Test the model with GPU id 0

The command above will load the model under ['models/msvd/'] and generate the prediction file. If you want to train the model (Please follow our paper for details.), please run

>./main.sh 0 train # Train the model with GPU id 0

It will train the model and save to ['models/msvd'].

Result

##Visualization **Example from NExT-QA dataset.

Citation

@inproceedings{xiao2021video,
      title={Video as Conditional Graph Hierarchy for Multi-Granular Question Answering}, 
      author={Junbin Xiao and Angela Yao and Zhiyuan Liu and Yicong Li and Wei Ji and Tat-Seng Chua},
      booktitle={Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI)},
      year={2022},
      pages={2804-2812}
}

Acknowledgement

Our feature extraction for object, frame appearance and motion are from BUTD and HCRN respectively. Many thanks the authors for their great work and code!