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

Context-VQA: Towards Context-Aware and Purposeful Visual Question Answering