论文标题
域适应能否使对象识别对每个人有效？
Can domain adaptation make object recognition work for everyone?
论文作者
论文摘要
尽管视觉识别方面取得了迅速的进展，但现代计算机视觉数据集大大超出了发达国家和在此类数据集中受过训练的模型，从而在看不见地理位置的图像上表现不佳。我们研究了整个地理位置上此类模型无监督的域适应性（UDA）的有效性。为此，我们首先策划了从现有数据集进行两个转变，以研究地理DA问题，并发现数据分布移动以外的新挑战：上下文转移，其中对象周围环境可能在整个地理位置以及亚种群变化中发生显着变化，其中类别内分布可能会发生变化。我们证明了地理DA上标准DA方法的效率低下，强调了对专业地理适应解决方案的需求，以应对使对象识别对每个人都起作用的挑战。
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.
