  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).

Computation and Language;Computer Vision and Pattern Recognition