Visual grounding (VG) is a challenging task to localize an object in an image
based on a textual description. Recent surge in the scale of VG models has
substantially improved performance, but also introduced a significant burden on
computational costs during fine-tuning. In this paper, we explore applying
parameter-efficient transfer learning (PETL) to efficiently transfer the
pre-trained vision-language knowledge to VG. Specifically, we propose
\textbf{DARA}, a novel PETL method comprising \underline{\textbf{D}}omain-aware
\underline{\textbf{A}}dapters (DA Adapters) and
\underline{\textbf{R}}elation-aware \underline{\textbf{A}}dapters (RA Adapters)
for VG. DA Adapters first transfer intra-modality representations to be more
fine-grained for the VG domain. Then RA Adapters share weights to bridge the
relation between two modalities, improving spatial reasoning. Empirical results
on widely-used benchmarks demonstrate that DARA achieves the best accuracy
while saving numerous updated parameters compared to the full fine-tuning and
other PETL methods. Notably, with only \textbf{2.13\%} tunable backbone
parameters, DARA improves average accuracy by \textbf{0.81\%} across the three
benchmarks compared to the baseline model. Our code is available at
https://github.com/liuting20/DARA.

基于文本描述对图像中的物体进行定位的视觉 grounding 是一项具有挑战性的任务。本文通过提出一种名为 DARA 的新的参数高效迁移学习方法，利用域感知适配器 (DA Adapters) 和关系感知适配器 (RA Adapters) 来提高图像 grounding 的准确性和空间推理能力，实现了最佳准确性并节省了大量参数。