Given the emergence of data science and machine learning throughout all aspects of
society, but particularly in the scientific domain, there is increased importance placed on …

Visualizations themselves have become a data format. Akin to other data formats such as
text and images, visualizations are increasingly created, stored, shared, and (re-) used with …

Charts are very popular for analyzing data. When exploring charts, people often ask a
variety of complex reasoning questions that involve several logical and arithmetic …

Captions that describe or explain charts help improve recall and comprehension of the
depicted data and provide a more accessible medium for people with visual disabilities …

High-quality visualization collections are beneficial for a variety of applications including
visualization reference and data-driven visualization design. The visualization community …

We introduce FigureQA, a visual reasoning corpus of over one million question-answer pairs
grounded in over 100,000 images. The images are synthetic, scientific-style figures from five …

Chart images are commonly used for data visualization. Automatically reading the chart
values is a key step for chart content understanding. Charts have a lot of variations in style …

Inspired by the great success of machine learning (ML), researchers have applied ML
techniques to visualizations to achieve a better design, development, and evaluation of …

The majority of visualizations on the web are still stored as raster images, making them
inaccessible to visually impaired users. We propose a deep‐neural‐network‐based …

We investigate how to automatically recover visual encodings from a chart image, primarily
using inferred text elements. We contribute an end‐to‐end pipeline which takes a bitmap …