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

2026-06-15 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

OR3 is a novel text-to-video retrieval method designed for operating room (OR) clips, addressing the challenge of implicit queries that require reasoning to identify safety-critical events. Unlike existing methods relying on global embeddings, OR3 converts video clips into action-driven digital twins (ActDTs), which group concurrent subject-action-object triplets within non-overlapping temporal intervals. It employs imagination-based retrieval, where a Large Language Model (LLM) generates hypothetical ActDTs from queries, enabling intra-modal matching via a single encoder trained with ActDT-tailored hard negatives. An evidence-grounded refinement step further revises imagined ActDTs based on discrepancies with top candidates. Evaluated on a benchmark derived from MM-OR, featuring 276 implicit queries across four reasoning categories over 386 robotic knee procedure clips, OR3 achieved 57.6 R@1 and 77.3 R@5, outperforming the strongest baseline and demonstrating fine-grained discrimination through temporal action reasoning.

Key takeaway

For Computer Vision Engineers developing retrieval systems for complex procedural videos, OR3's approach offers a significant advancement. If your current methods struggle with implicit, reasoning-heavy queries in domains like surgical safety, consider adopting action-driven digital twins and imagination-based retrieval. This strategy allows your system to achieve fine-grained discrimination between visually similar events, improving the accuracy of critical event identification and inspection in high-stakes environments.

Key insights

OR3 uses action-driven digital twins and LLM-based imagination for reasoning-intensive text-to-video retrieval in operating rooms.

Principles

• Implicit queries need reasoning, not just global embeddings.
• Action-driven digital twins enable fine-grained temporal reasoning.
• Imagination-based retrieval improves intra-modal matching.

Method

OR3 converts clips to ActDTs, then an LLM imagines ActDTs from queries. Intra-modal matching occurs via a single encoder, followed by evidence-grounded refinement using top candidates.

In practice

• Retrieve specific OR events for safety inspections.
• Identify pre-clipping steps in surgical videos.
• Discriminate visually similar surgical actions.

Topics

• Text-to-Video Retrieval
• Operating Room Safety
• Digital Twins
• Large Language Models
• Action Recognition
• Surgical Video Analysis

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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• Metadata-Aware Multi-Prompt Reasoning for Zero-Shot Accident Understanding — Decomposing zero-shot video understanding into distinct stages improves reasoning reliability.
• Training LLMs with Reinforcement Learning over Digital Twin Representations for Reasoning-Intensive Surgical VideoQA — Decoupling perception from reasoning in surgical video QA using RL-trained LLMs over digital twin representations improves multi-step reasoning.
• SToRe3D: Sparse Token Relevance in ViTs for Efficient Multi-View 3D Object Detection — Jointly sparsifying 2D image tokens and 3D object queries with relevance-aligned filtering significantly boosts multi-view 3D detection efficiency.
• Active Video Perception: Iterative Evidence Seeking for Agentic Long Video Understanding — Long video understanding agents should actively seek query-relevant evidence through iterative, goal-directed observation.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.