  In Domain Generalization (DG) tasks, models are trained by using only
training data from the source domains to achieve generalization on an unseen
target domain, this will suffer from the distribution shift problem. So it's
important to learn a classifier to focus on the common representation which can
be used to classify on multi-domains, so that this classifier can achieve a
high performance on an unseen target domain as well. With the success of cross
attention in various cross-modal tasks, we find that cross attention is a
powerful mechanism to align the features come from different distributions. So
we design a model named CADG (cross attention for domain generalization),
wherein cross attention plays a important role, to address distribution shift
problem. Such design makes the classifier can be adopted on multi-domains, so
the classifier will generalize well on an unseen domain. Experiments show that
our proposed method achieves state-of-the-art performance on a variety of
domain generalization benchmarks compared with other single model and can even
achieve a better performance than some ensemble-based methods.
