{"ID":6267174,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-13T01:02:08.706470581Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.08397","arxiv_id":"2607.08397","title":"Attribute Retrieving for Open-Vocabulary Endoscopic Compositional Referring Segmentation","abstract":"Referring Image Segmentation (RIS) aims to segment image regions specified by natural language, enabling fine-grained and controllable visual understanding. Extending RIS to endoscopic imagery, however, presents unique challenges, including scarce high-quality annotations and complex, domain-specific image-text relationships. Although recent vision-language models demonstrate strong cross-domain alignment, they often fail to capture fine-grained textual cues in endoscopic settings, resulting in suboptimal performance and limited generalization. To address these challenges, we introduce ReferEndoscopy, a large-scale benchmark for RIS in the endoscopy field. Building on this dataset, we propose the Attribute Retrieval-based Endoscopic-RIS (AR-ERIS) framework for open-vocabulary endoscopic compositional referring segmentation. AR-ERIS leverages attribute retrieval for open-vocabulary endoscopic compositional referring segmentation and is pretrained on the curated ReferEndoscopy dataset, achieving state-of-the-art performance with strong generalization across both simulated and real-world endoscopic data. The dataset and code will be publicly released upon completion of the review process.","short_abstract":"Referring Image Segmentation (RIS) aims to segment image regions specified by natural language, enabling fine-grained and controllable visual understanding. Extending RIS to endoscopic imagery, however, presents unique challenges, including scarce high-quality annotations and complex, domain-specific image-text relatio...","url_abs":"https://arxiv.org/abs/2607.08397","url_pdf":"https://arxiv.org/pdf/2607.08397v1","authors":"[\"Shun Liu\",\"Nan Xi\",\"Yang Liu\",\"Tianyu Luan\",\"Xuan Gong\",\"David Doermann\"]","published":"2026-07-09T12:21:06Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Language Model\"]","has_code":false}