Can domain adaptation make object recognition work for everyone?

TL;DR

This paper examines whether unsupervised domain adaptation can improve object recognition across different geographies, addressing challenges like context and subpopulation shifts that standard methods fail to handle.

Contribution

It introduces the Geographical DA problem, curates datasets for it, and highlights the limitations of existing DA methods in this context.

Findings

Standard DA methods are ineffective for Geographical DA.

Context and subpopulation shifts pose unique challenges.

Specialized geographical adaptation solutions are needed.

Abstract

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.