Figure Captioning with Relation Maps for Reasoning
Charles Chen, Ruiyi Zhang, Eunyee Koh, Sungchul Kim, Scott Cohen, Ryan A. Rossi
Abstract
Figures, such as line plots, pie charts, bar charts, are widely used to convey important information in a concise format. In this work, we investigate the problem of figure caption generation where the goal is to automatically generate a natural language description for a given figure. While natural image captioning has been studied extensively, figure captioning has received relatively little attention and remains a challenging problem. A successful solution to this task has many potential applications, such as: 1) automatic parsing large amount of figures in PDF document; 2) improving user experience by allowing figure content to be accessible to those with visual impairment. To solve this problem, we introduce a dataset FigCAP and propose novel attention mechanism. In order to solve the exposure bias issue, we further train the captioning model with sequence-level policy based on reinforcement learning, which directly optimizes evaluation metrics. Extensive experiments show that the proposed method outperforms the baselines, thus demonstrating a significant potential for automatic generating captions for figures.