Remote Sensing Visual Grounding
Overview
Remote sensing visual grounding studies how to locate the objects or regions described by text in satellite, aerial, and sometimes video imagery, often returning boxes or pixel masks. Common approaches align language with image features using proposals, cross-modal attention, and contrastive learning; newer systems also use vision-language models, segmentation or diffusion models, and stepwise spatial reasoning. Benchmarks such as DIOR-RSVG and RRSIS-D test localization across varied scenes, while work increasingly examines multi-entity, cross-sensor, open-vocabulary, and video settings. A key tension is that accurate answers or confident locations may not be supported by the right visual evidence, and models still struggle with fine-grained relations, clutter, and generalization across domains.
Latest papers
21 papers3 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.