Pinpointing Why Object Recognition Performance Degrades Across Income Levels and Geographies

Understand

Despite impressive advances in object-recognition, deep learning systems' performance degrades significantly across geographies and lower income levels raising pressing concerns of inequity.

• Addressing such performance gaps remains a challenge, as little is understood about why performance degrades across incomes or geographies.
• We take a step in this direction by annotating images from Dollar Street, a popular benchmark of geographically and economically diverse images, labeling each image with factors such as color, shape, and background.
• These annotations unlock a new granular view into how objects differ across incomes and regions.