- Abstract: This paper presents a new direction for the visual question answering task. Given an image with a simple linear algebraic equation system and a question in natural language based on the variables in the image, we propose an end-to-end deep learning model that produces accurate answers to questions pertaining to the value of the variables and other related questions. Modeling the problem of solving simple linear equations as a VQA task makes it interesting as the system now requires three kinds of understanding a) visual understanding to recognize digits, variables, operators and equal sign b) conceptual understanding of the symbolic meanings of coefficients, constants, variables, operators and equality and realizing the role of numbers as mathematical entities which can undergo mathematical operations and c) high level understanding of the interaction between the image and the questions in order to accurately answer them. We also create an open-source dataset for the same and compare the performance of our model with different baselines.
- Keywords: Linear equations, Deep Learning, Visual Question Answering, Mathematical Reasoning
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