Towards Increased Accessibility of Meme Images with the Help of Rich Face Emotion Captions
K R Prajwal, C. V. Jawahar, Ponnurangam Kumaraguru · 2019
In recent years, there has been an explosion in the number of memes being created and circulated in online social networks. Despite their rapidly increasing impact on how we communicate online, meme images are virtually inaccessible to the visually impaired users. Existing automated assistive systems that were primarily devised for natural photos in social media, overlook the specific fine-grained visual details in meme images. In this paper, we concentrate on describing one such prominent visual detail: the meme face emotion. We propose a novel automated method that enables visually impaired social media users to understand and appreciate meme face emotions with the help of rich textual captions. We first collect a challenging dataset of meme face emotion captions to support future research in face emotion understanding. We design a two-stage approach that significantly outperforms baseline approaches across all the standard captioning metrics and also generates richer discriminative captions. By validating our solution with the help of visually impaired social media users, we show that our emotion captions enable them to understand and appreciate one of the most popular classes of meme images encountered on the Internet for the first time. Code, data, and models are publicly available.