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Visual grounding focuses on establishing fine-grained alignment betweenvision and natural language, which has essential applications in multimodalreasoning systems. Existing methods use pre-trained query-agnostic visualbackbones to extract visual feature maps independently without considering thequery information. We argue that the visual features extracted from the visualbackbones and the features really needed for multimodal reasoning areinconsistent. One reason is that there are differences between pre-trainingtasks and visual grounding. Moreover, since the backbones are query-agnostic,it is difficult to completely avoid the inconsistency issue by training thevisual backbone end-to-end in the visual grounding framework. In this paper, wepropose a Query-modulated Refinement Network (QRNet) to address theinconsistent issue by adjusting intermediate features in the visual backbonewith a novel Query-aware Dynamic Attention (QD-ATT) mechanism and query-awaremultiscale fusion. The QD-ATT can dynamically compute query-dependent visualattention at the spatial and channel levels of the feature maps produced by thevisual backbone. We apply the QRNet to an end-to-end visual groundingframework. Extensive experiments show that the proposed method outperformsstate-of-the-art methods on five widely used datasets.

Shifting More Attention to Visual Backbone: Query-modulated Refinement Networks for End-to-End Visual Grounding