Reasoning Text-to-Video Retrieval for Operating Room Clips via Action-Driven Digital Twins 文章

ArXiv CS.CV2026-06-17NEWSen作者: Yiqing Shen, Hao Ding, Mathias Unberath

详细信息

来源站点
ArXiv CS.CV
作者
Yiqing Shen, Hao Ding, Mathias Unberath
文章类型
NEWS
语言
en
发布日期
2026-06-17

摘要

arXiv:2606.17298v1 Announce Type: new Abstract: Text-to-video retrieval in operating rooms (OR) is an enabling technology for OR safety, as it allows stakeholders to retrieve and inspect recordings of specific events. However, because the most safety-critical events may not follow the common structure, to unlock its full potential text-to-video retrieval must be able to handle implicit queries that require reasoning to identify the right video (e.g., the step right before clipping). However, existing methods rely on global embeddings that cannot reason over such queries. We propose OR3, a text-to-video retrieval method that converts clips into action-driven digital twins (ActDTs), grouping concurrent subject-action-object triplets under non-overlapping temporal intervals. Moreover, rather than cross-modal matching through paired encoders, OR3 performs imagination-based retrieval where an LLM generates hypothetical ActDTs from queries.

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