This repo contains code for our paper "Counterfactual Samples Synthesizing for Robust Visual Question Answering" This repo contains code modified from here,many thanks!

Make sure you are on a machine with a NVIDIA GPU and Python 2.7 with about 100 GB disk space.
h5py==2.10.0
pytorch==1.1.0
Click==7.0
numpy==1.16.5
tqdm==4.35.0

You can use

bash tools/download.sh

to download the data
and the rest of the data and trained model can be obtained from BaiduYun(passwd:3jot) or MEGADrive unzip feature1.zip and feature2.zip and merge them into data/rcnn_feature/
use

bash tools/process.sh 

to process the data

Run

CUDA_VISIBLE_DEVICES=0 python main.py --dataset cpv2 --mode q_v_debias --debias learned_mixin --topq 1 --topv -1 --qvp 5 --output [] --seed 0

to train a model

Run

CUDA_VISIBLE_DEVICES=0 python eval.py --dataset cpv2 --debias learned_mixin --model_state []

to eval a model

If you find this code useful, please cite the following paper:

@inproceedings{chen2020counterfactual,
title={Counterfactual Samples Synthesizing for Robust Visual Question Answering},
author={Chen, Long and Yan, Xin and Xiao, Jun and Zhang, Hanwang and Pu, Shiliang and Zhuang, Yueting},
booktitle={CVPR},
year={2020}
}