AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
[Submitted on 20 Oct 2026] Title:Complexity-Aware Surgical Video Reasoning Segmentation with Digital Twin and Context Memory View a PDF of the paper titled Complexity-Aware Surgical Video Reasoning Segmentation with Digital Twin and Context Memory, by Yiqing Shen and 1 other authors View PDF HTML (experimental) Abstract:Conventional surgical segmentation relies on closed-set categories or explicit text queries, which cannot resolve implicit queries that require reasoning about workflows, instrument functions, and temporal relationships. To address these limitations, we propose SurgReasonSeg, a complexity-aware method that brings reasoning segmentation over digital twin (DT) representations to surgical video. Specifically, SurgReasonSeg constructs DT representations from surgical foundation models and trains two low-rank adapters (LoRA) of one large language model with reinforcement learning (RL), each specialized for a different query complexity. A lightweight router, also trained with RL, selects between the two adapters to balance latency against accuracy. SurgReasonSeg also sets the temporal context with an adaptive sliding window conditioned on query complexity and workflow phase, and retrieves relevant historical cases from a surgical context memory by workflow similarity and procedural relevance. We further introduce EndoVis-ReasonSeg, a surgical reasoning segmentation benchmark with 1,460 training and 961 test samples across semantic, spatial, and temporal categories. On EndoVis-ReasonSeg, SurgReasonSeg improves J&F over the strongest baseline, ReSurgSAM2, by 0.178, 0.237, and 0.297 on semantic, spatial, and temporal queries, and it also achieves the best results on the Ref-EndoVis17 and Ref-EndoVis18 referring segmentation benchmarks. A backbone-matched attribution protocol separates the contribution of the upstream segmenter from that of the reasoning modules. Our method runs at 18.8 FPS averaged over the query distribution, enabling real-time reasoning segmentation in surgical videos. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2610.28776 [cs.CV] (or arXiv:2610.28776v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2610.28776 arXiv-issued DOI via DataCite (pending registration) Submission history From: Yiqing Shen [view email] [v1] Tue, 20 Oct 2026 18:10:55 UTC (4,151 KB) Full-text links: Access Paper: View a PDF of the paper titled Complexity-Aware Surgical Video Reasoning Segmentation with Digital Twin and Context Memory, by Yiqing Shen and 1 other authors View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CV new | recent | 2026-10 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)