C2I-DINO: Category-to-Instance Discrimination for Remote Sensing Visual Grounding

Remote sensing visual grounding (RSVG) aims to localize objects described by natural-language expressions in large-scale aerial images. Compared with conventional visual grounding, RSVG contains many repeated objects and long-range spatial relations, requiring a model to preserve the requested category cue while distinguishing the referred instance from similar objects. To address this challenge, we propose C2I-DINO, a category-to-instance framework built on Grounding DINO. It introduces Category-Focused Prompting (CFP), which retrieves learnable category-specific suffix vectors and appends them to the projected text memory before cross-modal fusion, and Spatial Instance Learning (SIL), which uses ranking and suppression losses to separate the matched query from high-response queries at incorrect locations during training. CFP strengthens the category cue, while SIL provides training-only supervision. Extensive experiments on RSVG, OPT-RSVG, and DIOR-RSVG demonstrate that C2I-DINO achieves the best reported results on most metrics under the evaluated protocols. Code is available at https://github.com/YongpengWen/C2I-DINO.git.