解决遥感图文生成中的空间错位问题，让生成图像更准确反映文本描述的地理布局。
Uni-RS: A Spatially Faithful Unified Understanding and Generation Model for Remote Sensing
- 将文本指令转化为空间布局规划，分离几何设计与图像合成过程
- 通过空间感知查询监督，强化模型对空间关系的理解与生成
- 引入几何一致的空间变换数据增强，提升生成结果的空间保真度
统一的遥感多模态模型存在显著的空间反转缺陷：虽能准确识别和描述图像中物体的位置，但在文本到图像生成中却难以忠实再现这些空间关系，而空间关系在遥感领域是核心语义信息。为此，我们提出Uni-RS，首个专为遥感设计的统一多模态模型，旨在显式解决理解与生成之间的空间不对称性。具体而言，首先引入显式的空间布局规划，将文本指令转换为空间布局计划，实现几何规划与视觉合成的解耦；其次，采用空间感知查询监督，引导可学习查询显式关注指令中指定的空间关系；最后，设计图像-标题空间布局变化机制，使模型暴露于系统性的、几何一致的空间变换中。在多个基准上的大量实验表明，该方法显著提升了文本到图像生成的空间保真度，同时保持了图像描述、视觉定位和VQA等多模态理解任务的强性能。
原文摘要 · Abstract (English)
Unified remote sensing multimodal models exhibit a pronounced spatial reversal curse: Although they can accurately recognize and describe object locations in images, they often fail to faithfully execute the same spatial relations during text-to-image generation, where such relations constitute core semantic information in remote sensing. Motivated by this observation, we propose Uni-RS, the first unified multimodal model tailored for remote sensing, to explicitly address the spatial asymmetry between understanding and generation. Specifically, we first introduce explicit Spatial-Layout Planning to transform textual instructions into spatial layout plans, decoupling geometric planning from visual synthesis. We then impose Spatial-Aware Query Supervision to bias learnable queries toward spatial relations explicitly specified in the instruction. Finally, we develop Image-Caption Spatial Layout Variation to expose the model to systematic geometry-consistent spatial transformations. Extensive experiments across multiple benchmarks show that our approach substantially improves spatial faithfulness in text-to-image generation, while maintaining strong performance on multimodal understanding tasks like image captioning, visual grounding, and VQA tasks.
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