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
∼
0.25 M images,
∼
0.76 M questions, and
∼
10 M 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
)....

VQA: Visual Question Answering: www.visualqa.org

10.1007/s11263-016-0966-6