Greedy Gradient Ensemble for De-biased VQA
Code release for "Greedy Gradient Ensemble for Robust Visual Question Answering" (ICCV 2021, Oral). GGE can extend to other tasks with dataset biases.
@inproceedings{han2015greedy,
title={Greedy Gradient Ensemble for Robust Visual Question Answering},
author={Han, Xinzhe and Wang, Shuhui and Su, Chi and Huang, Qingming and Tian, Qi},
booktitle={Proceedings of the IEEE international conference on computer vision},
year={2021}
}
Prerequisites
We use Anaconda to manage our dependencies . You will need to execute the following steps to install all dependencies:
-
Edit the value for
prefix
variable inrequirements.yml
file, by assigning it the path to conda environment -
Then, install all dependencies using:
conda env create -f requirements.yml
-
Change to the new environment:
bias
Data Setup
- Download UpDn features from google drive into
/data/detection_features
folder - Download questions/answers for VQAv2 and VQA-CPv2 by executing
bash tools/download.sh
- Download visual cues/hints provided in A negative case analysis of visual grounding methods for VQA into
data/hints
. Note that we use caption based hints for grounding-based method reproduction, CGR and CGW. - Preprocess process the data with
bash tools/process.sh
Training GGE
Run
CUDA_VISIBLE_DEVICES=0 python main.py --dataset cpv2 --mode MODE --debias gradient --topq 1 --topv -1 --qvp 5 --output []
to train a model. In main.py
, import base_model
for UpDn baseline; import base_model_ban as base_model
for BAN baseline; import base_model_block as base_model
for S-MRL baseline.
Set MODE
as gge_iter
and gge_tog
for our best performance model; gge_d_bias
and gge_q_bias
for single bias ablation; base
for baseline model.
Training ablations in Sec. 3 and Sec. 5
For models in Sec. 3, execute from train_ab import train
and import base_model_ab as base_model
in main.py
. Run
CUDA_VISIBLE_DEVICES=0 python main.py --dataset cpv2 --mode MODE --debias METHODS --topq 1 --topv -1 --qvp 5 --output []
METHODS learned_mixin
for LMH, MODE inv_sup
for inv_sup strategy, v_inverse
for inverse hint. Note that the results for HINT$_inv$ is obtained by running the code from A negative case analysis of visual grounding methods for VQA.
To test v_only model, import base_model_v_only as base_model
in main.py
.
To test RUBi and LMH+RUBi, run
CUDA_VISIBLE_DEVICES=0 python rubi_main.py --dataset cpv2 --mode MODE --output []
MODE updn
is for RUBi, lmh_rubi
is for LMH+RUBi.
Testing
For test stage, we output the overall Acc, CGR, CGW and CGD at threshold 0.2. change base_model to corresponding model in sensitivity.py
and run
CUDA_VISIBLE_DEVICES=0 python sensitivity.py --dataset cpv2 --debias METHOD --load_checkpoint_path logs/your_path --output your_path
Visualization
We provide visualization in visualization.ipynb
. If you want to see other visualization by yourself, download MS-COCO 2014 to data/images
.
Acknowledgements
This repo uses features from A negative case analysis of visual grounding methods for VQA. Some codes are modified from CSS and UpDn.