In recent years, several efforts have been aimed at improving the robustness
of vision models to domains and environments unseen during training. An
important practical problem pertains to models deployed in a new geography that
is under-represented in the training dataset, posing a direct challenge to fair
and inclusive computer vision. In this paper, we study the problem of
geographic robustness and make three main contributions. First, we introduce a
large-scale dataset GeoNet for geographic adaptation containing benchmarks
across diverse tasks like scene recognition (GeoPlaces), image classification
(GeoImNet) and universal adaptation (GeoUniDA). Second, we investigate the
nature of distribution shifts typical to the problem of geographic adaptation
and hypothesize that the major source of domain shifts arise from significant
variations in scene context (context shift), object design (design shift) and
label distribution (prior shift) across geographies. Third, we conduct an
extensive evaluation of several state-of-the-art unsupervised domain adaptation
algorithms and architectures on GeoNet, showing that they do not suffice for
geographical adaptation, and that large-scale pre-training using large vision
models also does not lead to geographic robustness. Our dataset is publicly
available at this https URL.

本文研究地理鲁棒性问题，提出 GeoNet 数据集并探究其背后的分布变化，表明现有无监督领域适应算法和大规模预训练不足以处理地理适应问题。