arXiv cs.AI· Weiyu Zhang, Yuan Hu, Yong Li, Yu Liu·· 3 天前AI 评分22
Uni-RS：面向遥感的空间忠实统一理解与生成模型
Uni-RS: A Spatially Faithful Unified Understanding and Generation Model for Remote Sensing
AI 导读
针对统一遥感多模态模型在文本到图像生成中难以忠实执行空间关系的"空间反转诅咒"，研究者提出 Uni-RS，通过显式空间布局规划、空间感知查询监督和图像-字幕空间布局变换三项设计，将几何规划与视觉合成解耦。实验显示该方法在多个基准上大幅提升生成的空间忠实度，同时在图像描述、视觉定位和 VQA 等理解任务上保持强劲表现。
正文 · 原文
Abstract: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.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2601.17673 [cs.CV] |
| (or arXiv:2601.17673v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2601.17673 arXiv-issued DOI via DataCite |
Submission history
From: Weiyu Zhang [view email]
[v1]
Sun, 25 Jan 2026 03:22:26 UTC (11,026 KB)
[v2]
Tue, 13 Oct 2026 03:36:12 UTC (13,819 KB)
来源：arXiv cs.AI · arxiv.org