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OK-VQA
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c77c43b6-8cd1-11eb-88ae-0e1f58d5e9a9
d43638e·
Jun 20, 2021 9:56 AM
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Overview

OK-VQA is a new dataset for visual question answering that requires methods which can draw upon outside knowledge to answer questions.

  • 14,055 open-ended questions
  • 5 ground truth answers per question
  • Manually filtered to ensure all questions require outside knowledge (e.g. from Wikipeida)
  • Reduced questions with most common answers to reduce dataset bias

Data Format

Input Questions Format

The questions are stored using the JSON file format.

The questions format has the following data structure:

{
"info" : info,
"task_type" : str,
"data_type": str,
"data_subtype": str,
"questions" : [question],
"license" : license
}

info {
"year" : int,
"version" : str,
"description" : str,
"contributor" : str,
"url" : str,
"date_created" : datetime
}

license{
"name" : str,
"url" : str
}

question{
"question_id" : int,
"image_id" : int,
"question" : str
}
  • task_type: type of annotations in the JSON file (OpenEnded).
  • data_type: source of the images (mscoco or abstract_v002).
  • data_subtype: type of data subtype (e.g. train2014/val2014/test2015 for mscoco, train2015/val2015 for abstract_v002).

Annotation Format

The annotations are stored using the JSON file format.

The annotations format has the following data structure:

{
"info" : info,
"data_type": str,
"data_subtype": str,
"annotations" : [annotation],
"license" : license
}

info {
"year" : int,
"version" : str,
"description" : str,
"contributor" : str,
"url" : str,
"date_created" : datetime
}

license{
"name" : str,
"url" : str
}

annotation{
"question_id" : int,
"image_id" : int,
"question_type" : str,
"answer_type" : str,
"answers" : [answer],
"multiple_choice_answer" : str
}

answer{
"answer_id" : int,
"answer" : str,
"answer_confidence": str
}
  • data_type: source of the images (mscoco or abstract_v002).

  • data_subtype: type of data subtype (e.g. train2014/val2014/test2015 for mscoco, train2015/val2015 for abstract_v002).

  • question_type: type of the question determined by the first few words of the question. For details, please see README.

  • answer_type: type of the answer. Currently, "yes/no", "number", and "other".

  • multiple_choice_answer: most frequent ground-truth answer.

  • answer_confidence:

    subject's confidence in answering the question. For details, please see Antol et al., ICCV 2015.

🎉Many thanks to Graviti Open Datasets for contributing the dataset
Basic Information
Application ScenariosNot Available
AnnotationsNot Available
TasksNot Available
LicenseCustom
Updated on2021-01-20 04:45:01
Metadata
Data TypeNot Available
Data Volume0
Annotation Amount0
File Size0.00B
Copyright Owner
Allen Institute for artificial intelligence
Annotator
Unknown