Grounding Across Views: Cross-View Referring Reasoning for Visual Grounding
Hao Gan, Liuqian Wang, Shuang Li, Yufei Gao, Wentao Chao, Chong Fu, Chengming Liu, Lei Shi
Abstract
Cross-view object geo-localization (CVOGL) aims to localize a target in a satellite image from an observation captured under another viewpoint. Existing methods typically specify the query with a click point or bounding box, marking the target region but underusing the semantic attributes and relations conveyed by natural language. Moreover, referring expressions are often viewpoint-dependent, making query-view descriptions difficult to ground directly in the satellite image. In this paper, we formulate cross-view visual grounding (CVVG), extending conventional point- or box-prompted CVOGL to instance grounding jointly guided by a referring expression and a visual exemplar. We present GAV (Grounding Across Views), a view-aware framework for selectively transferring visual and linguistic evidence across views. Its core is view-aware referring reasoning: target-centered descriptions are derived from the marked query image, checked for target-category consistency, and augmented with a direction-free role cue. Exemplar prompting, cross-view alignment, and scene-level discrimination ground this semantic evidence in a satellite instance. GAV achieves a new state of the art on MoP-UAV with 62.55% Acc@0.5. We also introduce CVOGL-Text, a benchmark for text-guided grounding from street- and drone-view queries to satellite images. GAV outperforms the click-point baseline by 7.09 and 6.79 Acc@0.25 points on the street- and drone-view splits, respectively, demonstrating robustness to larger viewpoint gaps. The code and annotations will be publicly released.