  Despite the rapid progress in deep visual recognition, modern computer vision
datasets significantly overrepresent the developed world and models trained on
such datasets underperform on images from unseen geographies. We investigate
the effectiveness of unsupervised domain adaptation (UDA) of such models across
geographies at closing this performance gap. To do so, we first curate two
shifts from existing datasets to study the Geographical DA problem, and
discover new challenges beyond data distribution shift: context shift, wherein
object surroundings may change significantly across geographies, and
subpopulation shift, wherein the intra-category distributions may shift. We
demonstrate the inefficacy of standard DA methods at Geographical DA,
highlighting the need for specialized geographical adaptation solutions to
address the challenge of making object recognition work for everyone.
