Anomalous Frame Detection Using VLM-Based Description Comparison for Extracting Expert-Specific Actions and Contextual Decision-Making Scenes with Intra-Video Self-Similarity

2026-07-12 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, short

Summary

The paper "Anomalous Frame Detection Using VLM-Based Description Comparison for Extracting Expert-Specific Actions and Contextual Decision-Making Scenes with Intra-Video Self-Similarity" introduces a novel method to extract expert know-how from maintenance videos. Addressing the challenge of transferring skills from experienced workers, this approach detects anomalous frames by comparing task videos. It leverages a Vision-Language Model (VLM) to generate frame-wise visual descriptions. Expert-specific actions are identified via frame similarity comparisons between two videos, while contextual decision-making scenes are extracted using segment similarities from intra-video self-similarity of descriptions. In simulated distribution board maintenance experiments across 27 scenarios, the method achieved extraction rates of 65% for action candidates and 61% for decision-scene candidates, significantly outperforming conventional methods which yielded 59% and 33% respectively.

Key takeaway

For Computer Vision Engineers developing skill transfer systems, this research offers a robust approach to capture nuanced expert knowledge beyond mere actions. You should consider integrating VLM-based description comparison and intra-video self-similarity analysis to identify both expert-specific actions and critical contextual decision-making scenes. This can significantly improve the fidelity of extracted know-how, as demonstrated by the 61% extraction rate for decision scenes, compared to 33% from conventional methods.

Key insights

A VLM-based method extracts expert actions and decision contexts from task videos by comparing visual descriptions.

Principles

• Expert know-how encompasses actions and contextual decisions.
• VLM-generated descriptions enable detailed video comparison.

Method

The method generates frame-wise VLM descriptions, then extracts expert actions via inter-video frame similarity and decision scenes via intra-video segment self-similarity.

In practice

• Implement VLM-based video comparison for skill transfer.
• Analyze intra-video self-similarity for decision context.

Topics

• Anomalous Frame Detection
• Vision-Language Models
• Expert Know-how Transfer
• Video Analysis
• Contextual Decision-Making
• Skill Transfer Systems

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.