Distribution Shift nested in Web Scraping : Adapting MS COCO for Inclusive Data

Popular benchmarks in Computer Vision suffer from a Western-centric bias that leads to a distribution shift problem when trying to deploy Machine Learning systems in developing countries. Palliating this problem using the same data generation methods in poorly represented countries will likely bring the same bias that were initially observed. In this paper, we propose an adaptation of the MS COCO data generation methodology that address this issue, and show how the web scraping methods nests geographical distribution shifts.