DroneSegEdit: Adapting Pretrained Generative Editors for UAV Segmentation with Language-Grounded Verification

Recent work has shown that pretrained image generators can support diverse visual tasks. Yet directly applying them to language-guided unmanned aerial vehicle (UAV) segmentation exposes a key limitation: a plausible mask may correspond to the wrong instance among small and repeated objects. We call this mismatch between mask rendering and expression-level target selection the Grounding-Rendering Gap. We present DroneSegEdit, which combines RGB proposal rendering with language-grounded, proposal-constrained verification to select supported foreground while retaining rendered geometry. DroneSegEdit adapts the open-weight LongCat-Image-Edit model using task-specific LoRA and fixed color-to-label decoding. For referring segmentation, a Language-Grounded Spatial Verifier (LGSV) reads hierarchical states from the frozen multimodal large language model backbone to derive expression-conditioned spatial evidence, which is mapped back to the rendered proposal without introducing foreground outside it. On RIS-LAD, DroneSegEdit reaches a new state of the art at 52.78 mIoU, 7.16 points above the previous best. It remains competitive overall on UAVid-RIS and VDD-RIS, with its clearest class-wise gains on compact, application-relevant categories, notably vehicles and humans. A separate verifier-free experiment extends the RGB interface to full-scene parsing: a task-specific adapter jointly renders all classes from one task-level instruction without enumerating class names or palette colors.