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

Related tags

Deep Learning IconQA
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

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Comments
  • Cannot reproduce the result in the paper

    Cannot reproduce the result in the paper

    Dear authors, I have some trouble reproducing the result in the paper using the training code. I followed the instructions in this repository and trained 3 models for 3 IconQA subtasks. I used the default training arguments. However, I am not able to reproduce the result in the paper.

    My reproduce result:

    • choose_img: 79.038
    • choose_txt: 67.369
    • fill_in_blank: 79.467

    The result from the paper:

    • choose_img: 82.66
    • choose_txt: 75.19
    • fill_in_blank: 83.62

    Could you provide the training arguments that I could use to reproduce the result in the paper?

    Thank you very much. Best regards.

    opened by lekhang4497 14
  • Mapping between the class ID and the class name in Icon645 Resnet model

    Mapping between the class ID and the class name in Icon645 Resnet model

    I would like to use your pre-trained Resnet model on the Icon645 dataset for classification.

    Using your pre-trained Icon645 Resnet model, I was able to output the 377-dimension softmax vector for classification (for 377 classes).

    I would like to know the mapping from the class ID to the class name in your Icon645 Resnet model. For example: 1 --> apple 2 --> acorn ...

    Is this mapping available? Thank you very much.

    opened by lekhang4497 2
Owner
Pan Lu
Computer Science
Pan Lu
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