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The paper analyzes the accuracy of publicly available object-recognition systems on a geographically diverse dataset.
Eskimo words for snow: A case study in the genesis and decay of an anthropological example
L. Martin · 1986
Earlier work this paper cites.
Wordnet: A lexical database for English
G. Miller · 1995
Earlier work this paper cites.
The feret evaluation methodology for face-recognition algorithms
P. J. Phillips, H. Moon, P. Rauss, and S. A. Rizvi · 1997
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R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
Earlier work this paper cites.
Microsoft COCO: Common objects in context
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
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Auditing black-box models for indirect influence
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Gender shades: Intersectional accuracy disparities in commercial gender classification
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