README.md

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paperswithcode_id: compguesswhat

Dataset Card for "compguesswhat"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://compguesswhat.github.io/
• Repository: More Information Needed
• Paper: More Information Needed
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 106.86 MB
• Size of the generated dataset: 258.55 MB
• Total amount of disk used: 365.41 MB

Dataset Summary

    CompGuessWhat?! is an instance of a multi-task framework for evaluating the quality of learned neural representations,
    in particular concerning attribute grounding. Use this dataset if you want to use the set of games whose reference
    scene is an image in VisualGenome. Visit the website for more details: https://compguesswhat.github.io

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

compguesswhat-original

• Size of downloaded dataset files: 102.24 MB
• Size of the generated dataset: 166.29 MB
• Total amount of disk used: 268.53 MB

An example of 'validation' looks as follows.

This example was too long and was cropped:

{
    "id": 2424,
    "image": "{\"coco_url\": \"http://mscoco.org/images/270512\", \"file_name\": \"COCO_train2014_000000270512.jpg\", \"flickr_url\": \"http://farm6.stat...",
    "objects": "{\"area\": [1723.5133056640625, 4838.5361328125, 287.44476318359375, 44918.7109375, 3688.09375, 522.1935424804688], \"bbox\": [[5.61...",
    "qas": {
        "answer": ["Yes", "No", "No", "Yes"],
        "id": [4983, 4996, 5006, 5017],
        "question": ["Is it in the foreground?", "Does it have wings?", "Is it a person?", "Is it a vehicle?"]
    },
    "status": "success",
    "target_id": 1197044,
    "timestamp": "2016-07-08 15:07:38"
}

compguesswhat-zero_shot

• Size of downloaded dataset files: 4.62 MB
• Size of the generated dataset: 92.26 MB
• Total amount of disk used: 96.88 MB

An example of 'nd_valid' looks as follows.

This example was too long and was cropped:

{
    "id": 0,
    "image": {
        "coco_url": "https://s3.amazonaws.com/nocaps/val/004e21eb2e686f40.jpg",
        "date_captured": "2018-11-06 11:04:33",
        "file_name": "004e21eb2e686f40.jpg",
        "height": 1024,
        "id": 6,
        "license": 0,
        "open_images_id": "004e21eb2e686f40",
        "width": 768
    },
    "objects": "{\"IsOccluded\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"IsTruncated\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"area\": [3...",
    "status": "incomplete",
    "target_id": "004e21eb2e686f40_30"
}

Data Fields

The data fields are the same among all splits.

compguesswhat-original

• id: a int32 feature.
• target_id: a int32 feature.
• timestamp: a string feature.
• status: a string feature.
• id: a int32 feature.
• file_name: a string feature.
• flickr_url: a string feature.
• coco_url: a string feature.
• height: a int32 feature.
• width: a int32 feature.
• width: a int32 feature.
• height: a int32 feature.
• url: a string feature.
• coco_id: a int32 feature.
• flickr_id: a string feature.
• image_id: a string feature.
• qas: a dictionary feature containing:
  • question: a string feature.
  • answer: a string feature.
  • id: a int32 feature.
• objects: a dictionary feature containing:
  • id: a int32 feature.
  • bbox: a list of float32 features.
  • category: a string feature.
  • area: a float32 feature.
  • category_id: a int32 feature.
  • segment: a dictionary feature containing:
    • feature: a float32 feature.

compguesswhat-zero_shot

• id: a int32 feature.
• target_id: a string feature.
• status: a string feature.
• id: a int32 feature.
• file_name: a string feature.
• coco_url: a string feature.
• height: a int32 feature.
• width: a int32 feature.
• license: a int32 feature.
• open_images_id: a string feature.
• date_captured: a string feature.
• objects: a dictionary feature containing:
  • id: a string feature.
  • bbox: a list of float32 features.
  • category: a string feature.
  • area: a float32 feature.
  • category_id: a int32 feature.
  • IsOccluded: a int32 feature.
  • IsTruncated: a int32 feature.
  • segment: a dictionary feature containing:
    • MaskPath: a string feature.
    • LabelName: a string feature.
    • BoxID: a string feature.
    • BoxXMin: a string feature.
    • BoxXMax: a string feature.
    • BoxYMin: a string feature.
    • BoxYMax: a string feature.
    • PredictedIoU: a string feature.
    • Clicks: a string feature.

Data Splits

compguesswhat-original

	train	validation	test
compguesswhat-original	46341	9738	9621

compguesswhat-zero_shot

	nd_valid	od_valid	nd_test	od_test
compguesswhat-zero_shot	5343	5372	13836	13300

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

Citation Information

        @inproceedings{suglia2020compguesswhat,
          title={CompGuessWhat?!: a Multi-task Evaluation Framework for Grounded Language Learning},
          author={Suglia, Alessandro, Konstas, Ioannis, Vanzo, Andrea, Bastianelli, Emanuele, Desmond Elliott, Stella Frank and Oliver Lemon},
          booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
          year={2020}
        }

Contributions

Thanks to @thomwolf, @aleSuglia, @lhoestq for adding this dataset.