Domain adaptation, which aims to transfer knowledge between domains, has been
well studied in many areas such as image classification and object detection.
However, for multi-modal tasks, conventional approaches rely on large-scale
pre-training. But due to the difficulty of acquiring multi-modal data,
large-scale pre-training is often impractical. Therefore, domain adaptation,
which can efficiently utilize the knowledge from different datasets (domains),
is crucial for multi-modal tasks. In this paper, we focus on the Referring
Expression Grounding (REG) task, which is to localize an image region described
by a natural language expression. Specifically, we propose a novel approach to
effectively transfer multi-modal knowledge through a specially
relation-tailored approach for the REG problem. Our approach tackles the
multi-modal domain adaptation problem by simultaneously enriching inter-domain
relations and transferring relations between domains. Experiments show that our
proposed approach significantly improves the transferability of multi-modal
domains and enhances adaptation performance in the REG problem.

本文提出了一种新颖的方法，通过一种特殊的关系定制方法来有效地转移多模态知识，以解决跨领域适配问题，实验结果表明，我们的方法显著提高了多模态领域的可转移性，并增强了 Referring Expression Grounding 问题的适配性能。