{"type":"ScholarlyArticle","title":"Uni-RS: A Spatially Faithful Unified Understanding and Generation Model for Remote Sensing","authors":[{"name":"Weiyu Zhang","slug":"weiyu-zhang","given":"Weiyu","family":"Zhang","affiliation":""},{"name":"Yuan Hu","slug":"yuan-hu","given":"Yuan","family":"Hu","affiliation":""},{"name":"Yong Li","slug":"yong-li","given":"Yong","family":"Li","affiliation":""},{"name":"Yu Liu","slug":"yu-liu","given":"Yu","family":"Liu","affiliation":""}],"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.","doi":"10.24963/ijcai.2026/231","url":"https://ijcai.org/proceedings/2026/231","pdf":"https://ijcai.org/proceedings/2026/0231.pdf","markdown":"https://ijcai.org/proceedings/2026/231.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/231","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":231,"pages":{"first":2073,"last":2081},"keywords":["Computer Vision: Vision, language and reasoning","Natural Language Processing: Language models","Computer Vision: Image and video synthesis and generation"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}