Visual Question Answering

Visual question answering (VQA) is an AI task that combines computer vision and natural language processing to answer questions about images. Given an image and a question in natural language, a VQA system generates a natural language answer.

Instead of only detecting objects, you ask specific questions ("What color is the shirt?" or "Is there a defect on the left side?") and get text answers. Datature Vi's VLMs generate freeform text responses, giving you natural and flexible answers for any question type.

New to Datature Vi?

Datature Vi lets you train a custom VLM for VQA on your own images. Learn what Datature Vi does or follow the quickstart.

Best for

By the end of this guide

Understand how VQA datasets pair images with question-answer annotations, enabling models to answer natural language questions about image content.

How VQA works

A VLM processes your image and question together through three stages:

Common use cases

VQA vs. other vision tasks

Task

Output

Best for

Use VQA when you need text answers. Use phrase grounding when you need spatial locations.

Tips for better VQA results

Practice

Good example

Avoid

Frequently asked questions

How VQA works in Datature Vi

The workflow for VQA in Datature Vi: create a VQA dataset, upload images, annotate with question-answer pairs, and train.

For deeper VQA concepts, see the VQA blog post and VQA glossary entry.