论文标题
CADG：基于跨注意领域概括的模型
CADG: A Model Based on Cross Attention for Domain Generalization
论文作者
论文摘要
在域的概括（DG）任务中，仅使用来自源域的训练数据来实现在看不见的目标域上的概括，这将遭受分配转移问题的困扰。因此，重要的是要学习一个分类器，以专注于可以用于多域进行分类的共同表示形式，以便该分类器也可以在看不见的目标域上实现高性能。在各种跨模式任务中交叉注意的成功，我们发现交叉注意是一种有力的机制，可以使特征来自不同的分布。因此，我们设计了一个名为CADG的模型（针对域泛化的交叉注意），其中交叉注意起着重要的作用，以解决分布转移问题。这样的设计使分类器可以在多域上采用，因此分类器将在看不见的域上很好地概括。实验表明，与其他单个模型相比，我们所提出的方法在各种领域的概括基准上实现了最新的性能，甚至可以比某些基于集合的方法获得更好的性能。
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.
