A new benchmark for Icon Question Answering (IconQA) and a large-scale icon dataset Icon645.

Related tags

Deep LearningIconQA
Overview

IconQA

License: CC BY-SA 4.0

About

IconQA is a new diverse abstract visual question answering dataset that highlights the importance of abstract diagram understanding and comprehensive cognitive reasoning in real-world problems.

iconqa examples

There are three different sub-tasks in IconQA:

  • 57,672 image choice MC questions
  • 31,578 text chioce MC questions
  • 18,189 fill-in-the-blank questions
Sub-Tasks Train Validation Test Total
Multi-image-choice 34,603 11,535 11,535 57,672
Multi-text-choice 18,946 6,316 6,316 31,578
Filling-in-the-blank 10,913 3,638 3,638 18,189

In addition to IconQA, we also present Icon645, a large-scale dataset of icons that cover a wide range of objects:

  • 645,687 colored icons
  • 377 different icon classes

icon_examples

For more details, you can find our website here and our paper here.

Download

Our dataset is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Please read the license before you use, change, or share our dataset.

You can download IconQA here. Or run the commands by:

cd data
wget https://iconqa2021.s3.us-west-1.amazonaws.com/iconqa.zip
unzip iconqa.zip

You can download Icon645 here. Or run the commands by:

cd data
wget https://iconqa2021.s3.us-west-1.amazonaws.com/icon645.zip
unzip icon645.zip

File structures for the IconQA dataset:

IconQA
|   LICENSE.md
|   metadata.json
|   pid2skills.json
|   pid_splits.json
|   problems.json
|   skills.json
└───test
│   │
│   └───choose_img
│   |   |
│   |   └───question_id
│   |   |   |   image.png
|   |   |   |   data.json
|   |   |   |   choice_0.png
|   |   |   |   choice_1.png
|   |   |   |   ...
|   |   |
|   |   └───question_id
|   |   |   ...
|   |   
|   └───choose_txt
|   |   |  
|   |   └───question_id
|   |   |   |   image.png
|   |   |   |   data.json
|   |   | 
|   |   └───question_id
|   |   |   ...
|   |
|   └───fill_in_blank
|       |  
|       └───question_id
|       |   |   image.png
|       |   |   data.json
|       | 
|       └───question_id
|       |   ...
|   
└───train
|   |   same as test
|   
└───val
    |   same as test

File structures for the Icon645 dataset:

Icon645
|   LICENCE.md
|   metadata.json
└───colored_icons_final
    |
    └───acorn
    |   |   image_id1.png
    |   |   image_id2.png
    |   |   ...
    |   
    └───airplane
    |   |   image_id3.png
    |   |   ...
    |      
    |   ...

Citation

If the paper or the dataset inspires you, please cite us:

@inproceedings{lu2021iconqa,
  title = {IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning},
  author = {Lu, Pan and Qiu, Liang and Chen, Jiaqi and Xia, Tony and Zhao, Yizhou and Zhang, Wei and Yu, Zhou and Liang, Xiaodan and Zhu, Song-Chun},
  booktitle = {Submitted to the 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks},
  year = {2021}
}

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

CC BY-NC-SA 4.0

Owner
Pan Lu
Computer Science
Pan Lu
GitHub Repository
Collection of machine learning related notebooks to share.

ML_Notebooks Collection of machine learning related notebooks to share. Notebooks GAN_distributed_training.ipynb In this Notebook, TensorFlow's tutori

Sascha Kirch 14 Dec 22, 2022
A large-scale benchmark for co-optimizing the design and control of soft robots, as seen in NeurIPS 2021.

Evolution Gym A large-scale benchmark for co-optimizing the design and control of soft robots. As seen in Evolution Gym: A Large-Scale Benchmark for E

121 Dec 14, 2022
This project is a re-implementation of MASTER: Multi-Aspect Non-local Network for Scene Text Recognition by MMOCR

This project is a re-implementation of MASTER: Multi-Aspect Non-local Network for Scene Text Recognition by MMOCR，which is an open-source toolbox based on PyTorch. The overall architecture will be sh

Jianquan Ye 82 Nov 17, 2022
ONNX-PackNet-SfM: Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX

Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX

Ibai Gorordo 14 Dec 09, 2022
Official code of CVPR 2021's PLOP: Learning without Forgetting for Continual Semantic Segmentation

PLOP: Learning without Forgetting for Continual Semantic Segmentation This repository contains all of our code. It is a modified version of Cermelli e

Arthur Douillard 116 Dec 14, 2022
A Tensorflow implementation of the Text Conditioned Auxiliary Classifier Generative Adversarial Network for Generating Images from text descriptions

A Tensorflow implementation of the Text Conditioned Auxiliary Classifier Generative Adversarial Network for Generating Images from text descriptions

Ayushman Dash 93 Aug 04, 2022
Template repository to build PyTorch projects from source on any version of PyTorch/CUDA/cuDNN.

The Ultimate PyTorch Source-Build Template Translations: 한국어 TL;DR PyTorch built from source can be x4 faster than a naïve PyTorch install. This repos

Joonhyung Lee/이준형 651 Dec 12, 2022
AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention

AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet buil

3.4k Jan 07, 2023
PyTorch3D is FAIR's library of reusable components for deep learning with 3D data

Introduction PyTorch3D provides efficient, reusable components for 3D Computer Vision research with PyTorch. Key features include: Data structure for

Facebook Research 6.8k Jan 01, 2023
Tensors and Dynamic neural networks in Python with strong GPU acceleration

PyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration Deep neural networks b

61.4k Jan 04, 2023
A hand tracking demo made with mediapipe where you can control lights with pinching your fingers and moving your hand up/down.

HandTrackingBrightnessControl A hand tracking demo made with mediapipe where you can control lights with pinching your fingers and moving your hand up

Teemu Laurila 19 Feb 12, 2022
Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. Graph Convolutional Networks for Hyperspectral Image Classification, IEEE TGRS, 2021.

Graph Convolutional Networks for Hyperspectral Image Classification Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot T

Danfeng Hong 154 Dec 13, 2022
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control

My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control

yobi byte 29 Oct 09, 2022
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning

Mammoth - An Extendible (General) Continual Learning Framework for Pytorch NEWS STAY TUNED: We are working on an update of this repository to include

AImageLab 277 Dec 28, 2022
EMNLP'2021: SimCSE: Simple Contrastive Learning of Sentence Embeddings

SimCSE: Simple Contrastive Learning of Sentence Embeddings This repository contains the code and pre-trained models for our paper SimCSE: Simple Contr

Princeton Natural Language Processing 2.5k Dec 29, 2022
Automatic learning-rate scheduler

AutoLRS This is the PyTorch code implementation for the paper AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly published

Yuchen Jin 33 Nov 18, 2022
KSAI Lite is a deep learning inference framework of kingsoft, based on tensorflow lite

KSAI Lite is a deep learning inference framework of kingsoft, based on tensorflow lite

80 Dec 27, 2022
Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)'

SCL Introduction Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)' We evaluated our approach using two baseline

34 Oct 08, 2022
A computational block to solve entity alignment over textual attributes in a knowledge graph creation pipeline.

How to apply? Create your config.ini file following the example provided in config.ini Choose one of the options below to run: Run with Python3 pip in

Scientific Data Management Group 3 Jun 23, 2022
Official implementation for paper: A Latent Transformer for Disentangled Face Editing in Images and Videos.

A Latent Transformer for Disentangled Face Editing in Images and Videos Official implementation for paper: A Latent Transformer for Disentangled Face

InterDigital 108 Dec 09, 2022