mlsanityData to 14 Oct 2026

Does Object Recognition Work for Everyone?

arXiv 1906.02659 ↗First posted 6 Jun 2019cs.CV cs.LGcited by 151

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Abstract (arXiv)

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.

Key facts. Cited by 151 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Jun 2019.

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Citing papers per year: 2021: 24; 2022: 42; 2023: 45; 2024: 24; 2025: 10; 2026: 6.

151 papers.

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