{"type":"ScholarlyArticle","title":"PDAR-RSITR: A Progressive Decoupling-Aggregation-Refinement Framework for Remote Sensing Image-Text Retrieval","authors":[{"name":"Shuhuai Wang","slug":"shuhuai-wang","given":"Shuhuai","family":"Wang","affiliation":""},{"name":"Songwei Pei","slug":"songwei-pei","given":"Songwei","family":"Pei","affiliation":""},{"name":"Bingfeng Liu","slug":"bingfeng-liu","given":"Bingfeng","family":"Liu","affiliation":""},{"name":"Duo Chai","slug":"duo-chai","given":"Duo","family":"Chai","affiliation":""},{"name":"Yuanzhou Huang","slug":"yuanzhou-huang","given":"Yuanzhou","family":"Huang","affiliation":""},{"name":"Jia Liu","slug":"jia-liu","given":"Jia","family":"Liu","affiliation":""},{"name":"Qian Li","slug":"qian-li","given":"Qian","family":"Li","affiliation":""},{"name":"Shangguang Wang","slug":"shangguang-wang","given":"Shangguang","family":"Wang","affiliation":""}],"abstract":"Remote Sensing Image-Text Retrieval (RSITR) aims to achieve precise retrieval between remote sensing images and textual descriptions. However, existing methods neglect the multi-dimensional cognitive attributes inherent in remote sensing data and struggle to handle them simultaneously, leading to suboptimal retrieval performance. In this paper, we propose a novel Progressive Decoupling-Aggregation-Refinement framework for RSITR (PDAR-RSITR) to comprehensively capture multi-dimensional cognitive attributes. Specifically, we adapt Multi-dimensional Cognitive Decoupling to learn features across cognitive attributes of different dimensions via multi-process clustering. Subsequently, we utilize Salient Cognitive Aggregation to select and aggregate the most salient attributes via dynamic routing. Furthermore, we propose Expert Collaborative Refinement to enhance critical cross-modal relationships via three complementary expert perspectives. Extensive experimental results demonstrate that PDAR-RSITR significantly outperforms existing state-of-the-art methods across multiple metrics.","doi":"10.24963/ijcai.2026/661","url":"https://ijcai.org/proceedings/2026/661","pdf":"https://ijcai.org/proceedings/2026/0661.pdf","markdown":"https://ijcai.org/proceedings/2026/661.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/661","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":"Natural Language Processing","track":"Main Track","number":661,"pages":{"first":5937,"last":5945},"keywords":["Natural Language Processing: Information retrieval and text mining"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}