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arXiv · 1906.02659

Does Object Recognition Work for Everyone?

Terrance DeVries·Ishan Misra·Changhan Wang·Laurens van der Maaten
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Abstract

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.

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Terrance DeVries, Ishan Misra, Changhan Wang, Laurens van der Maaten. 2019-06-18. Does Object Recognition Work for Everyone?. https://arxiv.org/abs/1906.02659

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