Causal-VidQA

The Causal-VidQA dataset contains 107,600 QA pairs from the Causal-VidQA dataset. The dataset aims to facilitate deeper video understanding towards video reasoning. In detail, we present the task of Causal-VidQA, which includes four types of questions ranging from scene description (description) to evidence reasoning (explanation) and commonsense reasoning (prediction and counterfactual). For commonsense reasoning, we set up a two-step solution by answering the question and providing a proper reason.

Here is an example from our dataset and the comparison between our dataset and other VisualQA datasets.

Example from our Causal-VidQA Dataset

Dataset	Visual Type	Visual Source	Annotation	Description	Explanation	Prediction	Counterfactual	#Video/Image	#QA	Video Length (s)
Motivation	Image	MS COCO	Man	✔	✔	✔	$\times$	10,191	-	-
VCR	Image	Movie Clip	Man	✔	✔	✔	$\times$	110,000	290,000	-
MovieQA	Video	Movie Stories	Auto	✔	✔	$\times$	$\times$	548	21,406	200
TVQA	Video	TV Show	Man	✔	✔	$\times$	$\times$	21,793	152,545	76
TGIF-QA	Video	TGIF	Auto	✔	$\times$	$\times$	$\times$	71,741	165,165	3
ActivityNet-QA	Video	ActivityNet	Man	✔	✔	$\times$	$\times$	5,800	58,000	180
Social-IQ	Video	YouTube	Man	✔	✔	$\times$	$\times$	1,250	7,500	60
CLEVRER	Video	Game Engine	Man	✔	✔	✔	✔	20,000	305,280	5
V2C	Video	MSR-VTT	Man	✔	✔	$\times$	$\times$	10,000	115,312	30
NExT-QA	Video	YFCC-100M	Man	✔	✔	$\times$	$\times$	5,440	52,044	44
Causal-VidQA	Video	Kinetics-700	Man	✔	✔	✔	✔	26,900	107,600	9

Comparison between our dataset and other VisualQA datasets

In this page, you can find the code of some SOTA VideoQA methods and the dataset for our CVPR conference paper.

Jiangtong Li, Li Niu and Liqing Zhang. From Representation to Reasoning: Towards both Evidence and Commonsense Reasoning for Video Question-Answering. CVPR, 2022. [paper link]

Download

Install

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

>conda create -n causal-vidqa python==3.6.12
>conda activate causal-vidqa
>git clone https://github.com/bcmi/Causal-VidQA
>pip install -r requirements.txt

Data Preparation

Please download the pre-computed features and QA annotations from Download 1-4. And place them in ['data/visual_feature'], ['data/text_feature'], ['data/split'] and ['data/QA']. Note that the Text annotation is package as QA.tar, you need to unpack it first before place it to ['data/QA'].

If you want to extract different video features and text features from our Causal-VidQA dataset, you can download the original data from Download 5 and do whatever your want to extract features.

Usage

Once the data is ready, you can easily run the code. First, to run these models with GloVe feature, you can directly train the B2A by:

>sh bash/train_glove.sh

Note that if you want to train the model with BERT feature, we suggest your to first load the BERT feature to sharedarray by:

>python dataset/load.py

and then train the B2A with BERT feature by:

>sh bash/train_bert.sh.

After the train shell file is conducted, you can find the the prediction file under ['results/model_name/model_prefix.json'] and you can evaluate the prediction results by:

>python eval_mc.py

You can also obtain the prediction by running:

>sh bash/eval.sh

The command above will load the model from ['experiment/model_name/model_prefix/model/best.pkl'] and generate the prediction file.

Hint: we have release a trained model for B2A method, please place this the trained weight in ['experiment/B2A/B2A/model/best.pkl'] and then make prediction by running:

>sh bash/eval.sh

(The results may be slightly different depending on the environments and random seeds.)

(For comparison, please refer to the results in our paper.)

Citation

@InProceedings{li2022from,
    author    = {Li, Jiangtong and Niu, Li and Zhang, Liqing},
    title     = {From Representation to Reasoning: Towards both Evidence and Commonsense Reasoning for Video Question-Answering},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022}
}

Acknowledgement

Our reproduction of the methods is mainly based on the Next-QA and other respective official repositories, we thank the authors to release their code. If you use the related part, please cite the corresponding paper commented in the code.

zhanglqxyz/Causal-VidQA