Infographic VQA

Infographics communicate information using a combination of textual, graphical and visual elements. This work explores the automatic understanding of infographic images by using a Visual Question Answering technique. To this end, we present InfographicVQA, a new dataset comprising a diverse collection of infographics and question-answer annotations. The questions require methods that jointly reason over the document layout, textual content, graphical elements, and data visualizations. We curate the dataset with an emphasis on questions that require elementary reasoning and basic arithmetic skills. For VQA on the dataset, we evaluate two Transformer-based strong baselines. Both the baselines yield unsatisfactory results compared to near perfect human performance on the dataset. The results suggest that VQA on infographics-images that are designed to communicate information quickly and clearly to human brain-is ideal for benchmarking machine understanding of complex document images. The dataset is available for download at docvqa.org

Automated summary

This work explores the automatic understanding of infographic images using a Visual Question Answering technique, and presents a diverse dataset called InfographicVQA. The dataset requires methods to reason over document layout, textual content, graphical elements, and data visualizations. Two Transformer-based baselines are evaluated, but they do not perform as well as humans on the dataset. The study suggests that VQA on infographics can serve as a benchmark for evaluating machine understanding of complex document images.