  Deep Learning has managed to push boundaries in a wide variety of tasks. One
area of interest is to tackle problems in reasoning and understanding, with an
aim to emulate human intelligence. In this work, we describe a deep learning
model that addresses the reasoning task of question-answering on categorical
plots. We introduce a novel architecture FigureNet, that learns to identify
various plot elements, quantify the represented values and determine a relative
ordering of these statistical values. We test our model on the FigureQA dataset
which provides images and accompanying questions for scientific plots like bar
graphs and pie charts, augmented with rich annotations. Our approach
outperforms the state-of-the-art Relation Networks baseline by approximately
$7\%$ on this dataset, with a training time that is over an order of magnitude
lesser.
