Answering Questions about Data Visualizations using Efficient Bimodal Fusion

They are ubiquitous in both scientific and business documents. Data visualizations are designed to be effective at conveying trends and comparisons in a glance, while also preserving salient details. Using computer vision to parse these visualizations can enable extraction of information that cannot be gleaned by solely studying a document's text. Despite the high potential payoff and tremendous practical value, this problem has received little attention until recently. In 2018, two datasets for answering questions about data visualizations were introduced along with new algorithms [15, 18]; however, there is considerable room for improvement. Here, we propose a novel algorithm that exceeds the state-of-the-art on both of these datasets by a large margin. Visual question answering (VQA) requires a system to answer questions about images [6, 27, 17].