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.

翻译：在域泛化任务中，模型仅使用源域的训练数据进行训练，以实现对未见目标域的泛化能力，但这一过程会面临分布偏移问题。因此，学习一个能够聚焦于跨域通用表示的分类器至关重要，该表示可用于多域分类，从而使分类器在未见目标域上同样具备高性能。鉴于交叉注意力在各类跨模态任务中取得的成功，我们发现交叉注意力是一种能够对齐不同分布特征的有效机制。为此，我们设计了名为CADG（交叉注意力域泛化）的模型，其中交叉注意力在解决分布偏移问题中起到了关键作用。这种设计使得分类器可适用于多域场景，从而在未见目标域上实现良好的泛化性能。实验表明，与各类单模型方法相比，我们的方法在多个域泛化基准测试中均达到了最先进水平，甚至优于某些基于集成的方法。