The paper analyzes the accuracy of publicly available object-recognition systems on a geographically diverse dataset. This dataset contains household items and was designed to have a more representative geographical coverage than commonly used image datasets in object recognition. We find that the systems perform relatively poorly on household items that commonly occur in countries with a low household income. Qualitative analyses suggest the drop in performance is primarily due to appearance differences within an object class (e.g., dish soap) and due to items appearing in a different context (e.g., toothbrushes appearing outside of bathrooms). The results of our study suggest that further work is needed to make object-recognition systems work equally well for people across different countries and income levels.
@article{arxiv.1906.02659,
title = {Does Object Recognition Work for Everyone?},
author = {Terrance DeVries and Ishan Misra and Changhan Wang and Laurens van der Maaten},
journal= {arXiv preprint arXiv:1906.02659},
year = {2019}
}