  Visual question answering (VQA) has the potential to make the Internet more
accessible in an interactive way, allowing people who cannot see images to ask
questions about them. However, multiple studies have shown that people who are
blind or have low-vision prefer image explanations that incorporate the context
in which an image appears, yet current VQA datasets focus on images in
isolation. We argue that VQA models will not fully succeed at meeting people's
needs unless they take context into account. To further motivate and analyze
the distinction between different contexts, we introduce Context-VQA, a VQA
dataset that pairs images with contexts, specifically types of websites (e.g.,
a shopping website). We find that the types of questions vary systematically
across contexts. For example, images presented in a travel context garner 2
times more "Where?" questions, and images on social media and news garner 2.8
and 1.8 times more "Who?" questions than the average. We also find that context
effects are especially important when participants can't see the image. These
results demonstrate that context affects the types of questions asked and that
VQA models should be context-sensitive to better meet people's needs,
especially in accessibility settings.

Computation and Language;Computer Vision and Pattern Recognition