DVQA: Understanding Data Visualizations via Question Answering
TL
TL;DR
DVQA is presented, a dataset that tests many aspects of bar chart understanding in a question answering framework and two strong baselines are proposed that perform considerably better than current VQA algorithms.
Abstract
Bar charts are an effective way to convey numeric information, but today's algorithms cannot parse them. Existing methods fail when faced with even minor variations in appearance. Here, we present DVQA, a dataset that tests many aspects of bar chart understanding in a question answering framework. Unlike visual question answering (VQA), DVQA requires processing words and answers that are unique to a particular bar chart. State-of-the-art VQA algorithms perform poorly on DVQA, and we propose two strong baselines that perform considerably better. Our work will enable algorithms to automatically extract numeric and semantic information from vast quantities of bar charts found in scientific publications, Internet articles, business reports, and many other areas.
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Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering
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FigureQA: An Annotated Figure Dataset for Visual Reasoning
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Data Augmentation for Visual Question Answering
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Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension
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