  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.
