Visual question answering is the task of returning the answer to a question about an image. A challenge is that different people often provide different answers to the same visual question. To our knowledge, this is the first work that aims to understand why. We propose a taxonomy of nine plausible reasons, and create two labelled datasets consisting of ~45,000 visual questions indicating which reasons led to answer differences. We then propose a novel problem of predicting directly from a visual question which reasons will cause answer differences as well as a novel algorithm for this purpose. Experiments demonstrate the advantage of our approach over several related baselines on two diverse datasets. We publicly share the datasets and code at https://vizwiz.org.
@article{arxiv.1908.04342,
title = {Why Does a Visual Question Have Different Answers?},
author = {Nilavra Bhattacharya and Qing Li and Danna Gurari},
journal= {arXiv preprint arXiv:1908.04342},
year = {2019}
}