arXiv (All)
open access
Complexity-Aware Surgical Video Reasoning Segmentation with Digital Twin and Context Memory
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 meth
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- LD-RSVIS: A Large-Scale and Diverse Benchmark for Referring Surgical Video Instrument Segmentation#1048816
- SurgOnAir: Hierarchy-Aware Real-Time Surgical Video Commentary#1327398
- Event-Aware Instructed Assistant for Referring Video Segmentation#310864
- SurgRAW: Multi-Agent Workflow with Chain of Thought Reasoning for Robotic Surgical Video Analysis#1012006
- Training-Free Temporal Abstraction for General Video Understanding#781207
Record · ID 1413017
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive