  Visual grounding aims to align visual information of specific regions of
images with corresponding natural language expressions. Current visual
grounding methods leverage pre-trained visual and language backbones
independently to obtain visual features and linguistic features. Although these
two types of features are then fused through elaborately designed networks, the
heterogeneity of the features renders them unsuitable for multi-modal
reasoning. This problem arises from the domain gap between the single-modal
pre-training backbones used in current visual grounding methods, which can
hardly be bridged by the traditional end-to-end training method. To alleviate
this, our work proposes an Empowering Pre-trained Model for Visual Grounding
(EpmVG) framework, which distills a multimodal pre-trained model to guide the
visual grounding task. EpmVG relies on a novel cross-modal distillation
mechanism that can effectively introduce the consistency information of images
and texts from the pre-trained model, reducing the domain gap in the backbone
networks, and thereby improving the performance of the model in the visual
grounding task. Extensive experiments have been conducted on five
conventionally used datasets, and the results demonstrate that our method
achieves better performance than state-of-the-art methods.
