Newest papers

MoG-VLM: Efficient Video Anomaly Detection through Motion-Guided Vision-Language Models

Khaled Waleed Dawoud, Muhammad Zaigham Zaheer, Khan Muhammad, Muhammad Haris Khan · 2026 · arXiv

DOI: 10.48550/arXiv.2610.31478 · open-access PDF

Japanese

ビジョン言語モデル(VLMs)はビデオ異常検出の強力な可能性を示していますが、密集したフレームレベルの推測により、リアルタイムでの展開には不適切です。既存の加速は、通常、コンテンツのダイナミクスを無視し、静的セグメントでの計算を無駄にしたり、ローカリゼーションされた異常を逃すことができます。 MoG-VLMは、コンテンツに気づくサムウェアを実行するTGM(Temporal Gradient Magnitude)を導入します。

Machine translation (Meta M2M100); the English original follows.

Abstract

Vision-Language Models (VLMs) have shown strong potential for video anomaly detection, but dense frame-level inference makes them impractical for real-time deployment. Existing acceleration commonly relies on uniform sampling, which ignores content dynamics and can either waste computation on static segments or miss localized anomalies. We propose MoG-VLM a motion-adaptive anomaly detection framework that leverages motion purely as a lightweight control signal to decide when VLM inference is necessary. To this end, we introduce Temporal Gradient Magnitude (TGM) which performs content-aware sam

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