Generalization to out-of-distribution (OOD) data is a capability natural to
humans yet challenging for machines to reproduce. This is because most learning
algorithms strongly rely on the i.i.d.~assumption on source/target data, which
is often violated in practice due to domain shift. Domain generalization (DG)
aims to achieve OOD generalization by using only source data for model
learning. Since first introduced in 2011, research in DG has made great
progresses. In particular, intensive research in this topic has led to a broad
spectrum of methodologies, e.g., those based on domain alignment,
meta-learning, data augmentation, or ensemble learning, just to name a few; and
has covered various vision applications such as object recognition,
segmentation, action recognition, and person re-identification. In this paper,
for the first time a comprehensive literature review is provided to summarize
the developments in DG for computer vision over the past decade. Specifically,
we first cover the background by formally defining DG and relating it to other
research fields like domain adaptation and transfer learning. Second, we
conduct a thorough review into existing methods and present a categorization
based on their methodologies and motivations. Finally, we conclude this survey
with insights and discussions on future research directions.Comment: v4: includes the word "vision" in the title; improves the
organization and clarity in Section 2-3; adds future directions; and mor