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VQA v2.0: Visual Question Answering Dataset

VQA v2.0 is a balanced visual question answering benchmark of open-ended questions over COCO images, with ~1.1M questions and 10 answers each (Goyal et al., CVPR 2017). This Potato config reproduces the answering task.

About this dataset

VQA v2.0 is an open-ended visual question answering benchmark introduced by Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh at CVPR 2017. Annotators read a natural language question about a real photo and write a free-form answer.

The questions are grounded in COCO images: 82,783 training, 40,504 validation, and 81,434 test images, roughly 204,000 photos in total. Across the splits there are about 1.1 million questions (443,757 train, 214,354 val, 447,793 test), and each question carries 10 ground-truth answers collected from independent workers.

The defining feature is its balanced complementary-pairs design. Every question is tied to a pair of similar images that yield two different answers, so a model cannot guess from language priors alone and must look at the image. With roughly twice the image-question pairs of VQA v1.0, it is widely used to train and evaluate multimodal models and to study language bias.

The Potato config below reproduces the VQA answering task: each item shows an image and its question, with a text field for the open-ended answer and a radio scheme for the yes/no and other question types.

Questions
~1.1M across splits
Answers per question
10 ground-truth
COCO images
~204,000
Train
443,757 questions / 82,783 images
Validation
214,354 questions / 40,504 images
Venue
CVPR 2017 (Goyal et al.)
Labels:outdoornatureurbanpeopleanimal+

Configuration Fileconfig.yaml

This Potato config reproduces the annotation task. Save it as config.yaml and run potato start config.yaml to try it.

yaml
# VQA v2.0 - Visual Question Answering
# Based on Goyal et al., CVPR 2017
# Paper: https://arxiv.org/abs/1612.00837
# Dataset: https://visualqa.org/
#
# This task presents an image and a natural language question about it.
# Annotators provide a free-form answer and, for yes/no questions,
# also select from a radio button group.
#
# Question Types:
# - yes/no: Questions that can be answered with yes or no
# - number: Questions asking about counts or quantities
# - other: Open-ended questions requiring descriptive answers
#
# Annotation Guidelines:
# 1. Look at the image carefully
# 2. Read the question
# 3. Provide a concise, accurate answer in the text field
# 4. For yes/no questions, also select the appropriate radio button
# 5. Answer based only on what you can see in the image

annotation_task_name: "VQA v2.0 - Visual Question Answering"
task_dir: "."

data_files:
  - sample-data.json

item_properties:
  id_key: "id"
  text_key: "text"

output_annotation_dir: "annotation_output/"
output_annotation_format: "json"

port: 8000
server_name: localhost

annotation_schemes:
  # Step 1: Free-form answer
  - annotation_type: text
    name: answer
    description: "Provide a concise answer to the question about the image"

  # Step 2: Yes/No/NA for applicable questions
  - annotation_type: radio
    name: yes_no_answer
    description: "For yes/no questions, select the appropriate answer. For other question types, select Not Applicable."
    labels:
      - "Yes"
      - "No"
      - "Not Applicable"
    keyboard_shortcuts:
      "Yes": "1"
      "No": "2"
      "Not Applicable": "3"
    tooltips:
      "Yes": "The answer to the yes/no question is yes"
      "No": "The answer to the yes/no question is no"
      "Not Applicable": "The question is not a yes/no question"

annotation_instructions: |
  You will be shown an image and a question about it. Your task is to:
  1. Study the image carefully.
  2. Read the question.
  3. Type a concise answer in the text field (1-3 words preferred).
  4. If the question is a yes/no question, also select Yes or No. Otherwise, select Not Applicable.

  Answer based only on what you can see in the image. Keep answers brief and specific.

html_layout: |
  <div style="padding: 15px; max-width: 800px; margin: auto;">
    <div style="background: #f8fafc; border: 1px solid #e2e8f0; border-radius: 8px; padding: 16px; margin-bottom: 16px; text-align: center;">
      <img src="{{image_url}}" style="max-width: 100%; max-height: 500px; border-radius: 4px;" alt="Image for question answering" />
    </div>
    <div style="background: #fefce8; border: 1px solid #fde68a; border-radius: 8px; padding: 16px; margin-bottom: 16px;">
      <strong style="color: #a16207; font-size: 18px;">Question:</strong>
      <p style="font-size: 17px; line-height: 1.6; margin: 8px 0 0 0; font-weight: 500;">{{text}}</p>
      <p style="font-size: 13px; color: #6b7280; margin: 8px 0 0 0;">Question type: {{question_type}}</p>
    </div>
  </div>

allow_all_users: true
instances_per_annotator: 50
annotation_per_instance: 2
allow_skip: true
skip_reason_required: false

Sample Datasample-data.json

json
[
  {
    "id": "vqa_001",
    "text": "What color is the fire hydrant?",
    "image_url": "images/vqa_001.jpg",
    "question_type": "other"
  },
  {
    "id": "vqa_002",
    "text": "Is the person wearing a hat?",
    "image_url": "images/vqa_002.jpg",
    "question_type": "yes/no"
  }
]

// ... and 8 more items

Get This Design

View on GitHub

Clone or download from the repository

Quick start:

git clone https://github.com/davidjurgens/potato-showcase.git
cd potato-showcase/image/visual-qa/vqav2-visual-question-answering
potato start config.yaml

Dataset & paper

Goyal et al., CVPR 2017

Citation (BibTeX)

bibtex
@inproceedings{goyal2017making,
    title = "Making the {V} in {VQA} Matter: Elevating the Role of Image Understanding in Visual Question Answering",
    author = "Goyal, Yash  and Khot, Tejas  and Summers-Stay, Douglas  and Batra, Dhruv  and Parikh, Devi",
    booktitle = "Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
    year = "2017",
    pages = "6904--6913"
}

Details

Annotation Types

textradio

Domain

Computer VisionVisual Question Answering

Use Cases

Visual QAImage UnderstandingMultimodal Reasoning

Tags

vqavisual-qaimage-understandingmultimodalcvpr2017

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