# VQA: Visual Question Answering

arXiv: https://arxiv.org/abs/1505.00468 · HTML: https://mlsanity.com/paper/1505.00468/

## Key facts

- Cited by 2,506 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2015.

## Built on by (most cited first, 30 of 2506)

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## Abstract (arXiv)

We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing ~0.25M images, ~0.76M questions, and ~10M answers (www.visualqa.org), and discuss the information it provides. Numerous baselines and methods for VQA are provided and compared with human performance. Our VQA demo is available on CloudCV (http://cloudcv.org/vqa).