PyTorch implementation of "Debiased Visual Question Answering from Feature and Sample Perspectives" (NeurIPS 2021)

Related tags

Deep LearningD-VQA
Overview

D-VQA

We provide the PyTorch implementation for Debiased Visual Question Answering from Feature and Sample Perspectives (NeurIPS 2021).

D-VQA

Dependencies

  • Python 3.6
  • PyTorch 1.1.0
  • dependencies in requirements.txt
  • We train and evaluate all of the models based on one TITAN Xp GPU

Getting Started

Installation

  1. Clone this repository:

     git clone https://github.com/Zhiquan-Wen/D-VQA.git
     cd D-VQA
    
  2. Install PyTorch and other dependencies:

     pip install -r requirements.txt
    

Download and preprocess the data

cd data 
bash download.sh
python preprocess_features.py --input_tsv_folder xxx.tsv --output_h5 xxx.h5
python feature_preprocess.py --input_h5 xxx.h5 --output_path trainval 
python create_dictionary.py --dataroot vqacp2/
python preprocess_text.py --dataroot vqacp2/ --version v2
cd ..

Training

  • Train our model
CUDA_VISIBLE_DEVICES=0 python main.py --dataroot data/vqacp2/ --img_root data/coco/trainval_features --output saved_models_cp2/ --self_loss_weight 3 --self_loss_q 0.7
  • Train the model with 80% of the original training set
CUDA_VISIBLE_DEVICES=0 python main.py --dataroot data/vqacp2/ --img_root data/coco/trainval_features --output saved_models_cp2/ --self_loss_weight 3 --self_loss_q 0.7 --ratio 0.8 

Evaluation

  • A json file of results from the test set can be produced with:
CUDA_VISIBLE_DEVICES=0 python test.py --dataroot data/vqacp2/ --img_root data/coco/trainval_features --checkpoint_path saved_models_cp2/best_model.pth --output saved_models_cp2/result/
  • Compute detailed accuracy for each answer type:
python comput_score.py --input saved_models_cp2/result/XX.json --dataroot data/vqacp2/

Pretrained model

A well-trained model can be found here. The test results file produced by it can be found here and its performance is as follows:

Overall score: 61.91
Yes/No: 88.93 Num: 52.32 other: 50.39

Reference

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

@inproceedings{D-VQA,
  title     = {Debiased Visual Question Answering from Feature and Sample Perspectives},
  author    = {Zhiquan Wen, 
               Guanghui Xu, 
               Mingkui Tan, 
               Qingyao Wu, 
               Qi Wu},
  booktitle = {NeurIPS},
  year = {2021}
}

Acknowledgements

This repository contains code modified from SSL-VQA, thank you very much!

Besides, we thank Yaofo Chen for providing MIO library to accelerate the data loading.

Comments
  • Questions about the code

    Questions about the code

    Thank you very much for providing the code, but I still have two questions that I did not understand well.

    1. A module, BDM, is used to capture negative bias, but this module only includes a multi-layer perceptron. Then how to ensure the features captured by this multi-layer perceptron are negative bias?
    2. On the left of Figure 2 of the paper, there are no backward gradient of the question-to-answer and the vision-to-answer branches. Where did it reflect in the code?
    opened by darwann 4
  • CVE-2007-4559 Patch

    CVE-2007-4559 Patch

    Patching CVE-2007-4559

    Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

    If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

    opened by TrellixVulnTeam 0
  • LXMERT numbers

    LXMERT numbers

    Hi, I wish to reproduce the LXMERT(LXMERT without D-VQA) numbers reported in the paper. It would be helpful if you could provide me with a way to do this using your code. I tried using the original LXMERT code, but I am not able to get the numbers reported in your paper on the VQA-CP2 dataset.

    opened by Vaidehi99 0
  • Download trainval_36.zip error

    Download trainval_36.zip error

    Hi, thank you for your work on this.

    I keep getting a download error when downloading the trainval_36.zip file. Is there another link I can use to download this?

    Thanks in advance!

    opened by chojw 0
  • 关于box和image的对齐问题

    关于box和image的对齐问题

    您好,我将box的注释解开后,重新生成特征,然后将其绘制出来,但是明显感觉有偏差,不知道您是否可以提供一份绘图的代码。 image 下面是我的代码 def plot_rect(image, boxes): img = Image.fromarray(np.uint8(image)) draw = ImageDraw.Draw(img) for k in range(2): box = boxes[k,:] print(box) drawrect(draw, box, outline='green', width=3) img = np.asarray(img) return img def drawrect(drawcontext, xy, outline=None, width=0): x1, y1, x2, y2 = xy points = (x1, y1), (x2, y1), (x2, y2), (x1, y2), (x1, y1) drawcontext.line(points, fill=outline, width=width)

    opened by LemonQC 0
Owner
Zhiquan Wen
Zhiquan Wen
A scikit-learn compatible neural network library that wraps PyTorch

A scikit-learn compatible neural network library that wraps PyTorch. Resources Documentation Source Code Examples To see more elaborate examples, look

4.9k Jan 03, 2023
A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes

A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes

443 Jan 06, 2023
Final report with code for KAIST Course KSE 801.

Orthogonal collocation is a method for the numerical solution of partial differential equations

Chuanbo HUA 4 Apr 06, 2022
A curated list of long-tailed recognition resources.

Awesome Long-tailed Recognition A curated list of long-tailed recognition and related resources. Please feel free to pull requests or open an issue to

Zhiwei ZHANG 542 Jan 01, 2023
Do you like Quick, Draw? Well what if you could train/predict doodles drawn inside Streamlit? Also draws lines, circles and boxes over background images for annotation.

Streamlit - Drawable Canvas Streamlit component which provides a sketching canvas using Fabric.js. Features Draw freely, lines, circles, boxes and pol

Fanilo Andrianasolo 325 Dec 28, 2022
[CVPR 2020] GAN Compression: Efficient Architectures for Interactive Conditional GANs

GAN Compression project | paper | videos | slides [NEW!] GAN Compression is accepted by T-PAMI! We released our T-PAMI version in the arXiv v4! [NEW!]

MIT HAN Lab 1k Jan 07, 2023
An energy estimator for eyeriss-like DNN hardware accelerator

Energy-Estimator-for-Eyeriss-like-Architecture- An energy estimator for eyeriss-like DNN hardware accelerator This is an energy estimator for eyeriss-

HEXIN BAO 2 Mar 26, 2022
This repo contains the official implementations of EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis

EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis This repo contains the official implementations of EigenDamage: Structured Prunin

Chaoqi Wang 107 Apr 20, 2022
Pytorch implementation of MixNMatch

MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation [Paper] Yuheng Li, Krishna Kumar Singh, Utkarsh Ojha, Yong Jae Le

910 Dec 30, 2022
An efficient framework for reinforcement learning.

rl: An efficient framework for reinforcement learning Requirements Introduction PPO Test Requirements name version Python =3.7 numpy =1.19 torch =1

16 Nov 30, 2022
EgGateWayGetShell py脚本

EgGateWayGetShell_py 免责声明 由于传播、利用此文所提供的信息而造成的任何直接或者间接的后果及损失,均由使用者本人负责,作者不为此承担任何责任。 使用 python3 eg.py urls.txt 目标 title:锐捷网络-EWEB网管系统 port:4430 漏洞成因 ?p

榆木 61 Nov 09, 2022
On Effective Scheduling of Model-based Reinforcement Learning

On Effective Scheduling of Model-based Reinforcement Learning Code to reproduce the experiments in On Effective Scheduling of Model-based Reinforcemen

laihang 8 Oct 07, 2022
Styled Augmented Translation

SAT Style Augmented Translation Introduction By collecting high-quality data, we were able to train a model that outperforms Google Translate on 6 dif

139 Dec 29, 2022
Implementation of the famous Image Manipulation\Forgery Detector "ManTraNet" in Pytorch

Who has never met a forged picture on the web ? No one ! Everyday we are constantly facing fake pictures touched up in Photoshop but it is not always

Rony Abecidan 77 Dec 16, 2022
The code for the CVPR 2021 paper Neural Deformation Graphs, a novel approach for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects.

Neural Deformation Graphs Project Page | Paper | Video Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction Aljaž Božič, Pablo P

Aljaz Bozic 134 Dec 16, 2022
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

CNTK Chat Windows build status Linux build status The Microsoft Cognitive Toolkit (https://cntk.ai) is a unified deep learning toolkit that describes

Microsoft 17.3k Dec 29, 2022
RCDNet: A Model-driven Deep Neural Network for Single Image Rain Removal (CVPR2020)

RCDNet: A Model-driven Deep Neural Network for Single Image Rain Removal (CVPR2020) Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng [PDF] [Supplementary M

Hong Wang 6 Sep 27, 2022
A more easy-to-use implementation of KPConv

A more easy-to-use implementation of KPConv This repo contains a more easy-to-use implementation of KPConv based on PyTorch. Introduction KPConv is a

Zheng Qin 35 Dec 14, 2022
Colour detection is necessary to recognize objects, it is also used as a tool in various image editing and drawing apps.

Colour Detection On Image Colour detection is the process of detecting the name of any color. Simple isn’t it? Well, for humans this is an extremely e

Astitva Veer Garg 1 Jan 13, 2022
Контрольная работа по математическим методам машинного обучения

ML-MathMethods-Test Контрольная работа по математическим методам машинного обучения. Вычисление основных статистик, диаграмм и графиков, проверка разл

Stas Ivanovskii 1 Jan 06, 2022