Learning domain-invariant representations is important to train a model that
can generalize well to unseen target task domains. Text descriptions inherently
contain semantic structures of concepts and such auxiliary semantic cues can be
used as effective pivot embedding for domain generalization problems. Here, we
use multimodal graph representations, fusing images and text, to get
domain-invariant pivot embeddings by considering the inherent semantic
structure between local images and text descriptors. Specifically, we aim to
learn domain-invariant features by (i) representing the image and text
descriptions with graphs, and by (ii) clustering and matching the graph-based
image node features into textual graphs simultaneously. We experiment with
large-scale public datasets, such as CUB-DG and DomainBed, and our model
achieves matched or better state-of-the-art performance on these datasets. Our
code will be publicly available upon publication.

通过融合图像和文本的多模态图形表示，我们通过考虑图像和文本描述符之间内在的语义结构得到领域不变的关键嵌入，从而实现领域泛化问题中的域不变特征学习。我们的模型在 CUB-DG 和 DomainBed 等大规模公共数据集上实验，取得了与或更好于这些数据集上的最新性能。