# Referring Expression Segmentation

In aerial drone imagery, a referred target often covers only tens of pixels and sits among visually similar vehicles or people, so a generative editor can draw a plausible mask around the wrong instance. DroneSegEdit freezes the open-weight LongCat-Image-Edit, trains only a lightweight LoRA to render candidate regions, then uses a language-grounded spatial verifier to strip foreground inside that candidate that the referring expression does not support. On the complete official RIS-LAD test set it reaches 52.78 mIoU, 7.16 points above the previous best ReSaP-8B, and stays competitive overall on UAVid-RIS and VDD-RIS, with the clearest gains on compact vehicles and humans.

In aerial drone imagery, a referred target often covers only tens of pixels and sits among visually similar vehicles or people, so a generative editor can draw a plausible mask around the wrong instance. DroneSegEdit freezes the open-weight LongCat-Image-Edit, trains only a lightweight LoRA to render candidate regions, then uses a language-grounded spatial verifier to strip foreground inside that candidate that the referring expression does not support. On the complete official RIS-LAD test set it reaches 52.78 mIoU, 7.16 points above the previous best ReSaP-8B, and stays competitive overall on UAVid-RIS and VDD-RIS, with the clearest gains on compact vehicles and humans.

Xiaomi released Xiaomi-Lens-0, a 4B multimodal model that decodes region masks from its own visual representations instead of relying on an extra segmentation encoder. Earlier multimodal models either took spatial detail from a separate segmentation encoder or kept the vision encoder frozen, so mask loss never reached the shared features. Xiaomi-Lens-0 pairs vision-encoder patch features with visual latents, the language model's states at generated mask tokens, in a lightweight decoder, so pixel-level supervision reaches the backbone shared with language generation; it gains 5.6 points on average over Qwen3.5 across all eight RefCOCO, RefCOCO+, and RefCOCOg splits and reaches the highest segmentation cIoU among compared models.

Xiaomi released Xiaomi-Lens-0, a 4B multimodal model that decodes region masks from its own visual representations instead of relying on an extra segmentation encoder. Earlier multimodal models either took spatial detail from a separate segmentation encoder or kept the vision encoder frozen, so mask loss never reached the shared features. Xiaomi-Lens-0 pairs vision-encoder patch features with visual latents, the language model's states at generated mask tokens, in a lightweight decoder, so pixel-level supervision reaches the backbone shared with language generation; it gains 5.6 points on average over Qwen3.5 across all eight RefCOCO, RefCOCO+, and RefCOCOg splits and reaches the highest segmentation cIoU among compared models.