Generalizing visual recognition models trained on a single distribution to
unseen input distributions (i.e. domains) requires making them robust to
superfluous correlations in the training set. In this work, we achieve this
goal by altering the training images to simulate new domains and imposing
consistent visual attention across the different views of the same sample. We
discover that the first objective can be simply and effectively met through
visual corruptions. Specifically, we alter the content of the training images
using the nineteen corruptions of the ImageNet-C benchmark and three additional
transformations based on Fourier transform. Since these corruptions preserve
object locations, we propose an attention consistency loss to ensure that class
activation maps across original and corrupted versions of the same training
sample are aligned. We name our model Attention Consistency on Visual
Corruptions (ACVC). We show that ACVC consistently achieves the state of the
art on three single-source domain generalization benchmarks, PACS, COCO, and
the large-scale DomainNet.

通过使用图像数据的变形，以及同一样本不同视角间的一致性注意力以强化单一数据分布训练出来的视觉识别模型对不同数据分布（即域）的鲁棒性，并命名该模型为 ACVC。研究表明，ACVC 在 PACS、COCO 和 DomainNet 三个单源域泛化基准测试中均获得了最新的最佳效果。