Uni-RS: 一种用于遥感的具有空间忠实性的统一理解和生成模型
Uni-RS: A Spatially Faithful Unified Understanding and Generation Model for Remote Sensing
AI总结：
Uni-RS通过空间布局规划、空间感知查询监督和图像描述空间布局变化，提升遥感文本到图像生成的空间忠实性，同时保持多模态理解任务的性能。
摘要：
统一的遥感多模态模型表现出显著的空间反转诅咒：尽管它们能够准确识别和描述图像中的物体位置，但在文本到图像生成过程中，往往无法忠实执行相同的空关系，而这些关系在遥感中是核心语义信息。受此观察启发，我们提出了Uni-RS，这是首个针对遥感设计的统一多模态模型，旨在明确解决理解和生成之间的空间不对称性。具体而言，我们首先引入了显式的空间布局规划，将文本指令转换为空间布局计划，将几何规划与视觉合成解耦。然后，我们施加了空间感知查询监督，使可学习的查询偏向于指令中明确指定的空间关系。最后，我们开发了图像描述空间布局变化，使模型系统地接触一致的几何变换。在多个基准上的广泛实验表明，我们的方法显著提高了文本到图像生成中的空间忠实性，同时在图像描述、视觉定位和VQA等多模态理解任务中保持了强大的性能。
英文摘要：
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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