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

CADG: A Model Based on Cross Attention for Domain Generalization