Showcase/Visual Question Answering
beginnerimage

Visual Question Answering

Answer questions about images for VQA dataset creation.

🖼️

image annotation

Configuration Fileconfig.yaml

task_name: "Visual Question Answering"
task_description: "Answer the question about the image."
task_dir: "."
port: 8000

data_files:
  - "sample-data.json"

item_properties:
  id_key: id
  image_key: image_url
  context_key: question

annotation_schemes:
  - annotation_type: text
    name: answer
    description: "Provide a concise answer to the question"
    required: true

  - annotation_type: radio
    name: confidence
    description: "How confident are you in your answer?"
    labels:
      - "Very confident"
      - "Somewhat confident"
      - "Not confident"
    required: true

output_annotation_dir: "output/"
output_annotation_format: "json"

Sample Datasample-data.json

[
  {
    "id": "1",
    "image_url": "https://images.unsplash.com/photo-1560807707-8cc77767d783?w=640",
    "question": "What color is the dog?"
  },
  {
    "id": "2",
    "image_url": "https://images.unsplash.com/photo-1449824913935-59a10b8d2000?w=640",
    "question": "What time of day does this appear to be?"
  }
]

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/visual-qa
potato start config.yaml

Details

Annotation Types

textradioimage

Domain

Computer VisionNLP

Use Cases

Visual QAMultimodal

Tags

vqavisual-qamultimodalimage

Found an issue or want to improve this design?

Open an Issue

Related Designs

Image Classification

Multi-class image classification with thumbnail preview and zoom controls.

radioimage

Commonsense Inference (ATOMIC 2020)

Annotate commonsense inferences about events, mental states, and social interactions. Based on ATOMIC 2020 (Hwang et al., AAAI 2021). Generate if-then knowledge about causes, effects, intents, and reactions.

radiotext

Commonsense QA Explanation (ECQA)

Annotate explanations for commonsense QA with positive and negative properties. Based on ECQA (Aggarwal et al., ACL 2021). Explain why an answer is correct and why others are wrong.

radiotext