ReactVAU：一個用於串流視訊異常理解的慢-快解耦框架

摘要

在本文中，我們提出 ReactVAU，一個用於即時串流視訊異常理解（VAU）的慢-快解耦框架。現有 VAU 方法依賴使用全域時間取樣的離線推論，這違反因果性，並且阻礙其在即時監控串流中的部署。相反地，通用串流視訊模型滿足因果存取，但在記憶壓縮過程中會稀釋罕見的瞬態異常，並且經常在長時間正常區間內均勻地呼叫重量級 MLLM。ReactVAU 透過三個協同組件來填補此缺口：一個基於空間網格摺疊（SGF）的輕量級快速偵測模組，用於持續異常過濾；一個異常感知持久記憶（AAPM），用於保護關鍵視覺線索免於時間衰減；以及一個重量級慢速推理模組，其在正常串流期間保持休眠，僅由可疑事件喚醒以進行語意驗證與因果描述。在多個基準上的大量實驗顯示，ReactVAU 在嚴格串流限制下運作，同時在異常偵測與因果推理方面均達到具競爭力的效能，並透過最小化重量級 MLLM 呼叫顯著提升計算效率。專案頁面位於 https://huiyuiui.github.io/React_VAU/

English

In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Existing VAU methods rely on offline inference with global temporal sampling, which violates causality and prevents deployment in live surveillance streams. Conversely, general streaming video models satisfy causal access but dilute rare transient anomalies during memory compression and often invoke heavyweight MLLMs uniformly over long normal intervals. React VAU addresses this gap with three synergistic components: a lightweight Fast Detection Module based on Spatial Grid Folding (SGF) for continuous anomaly filtering; an Anomaly-Aware Persistent Memory (AAPM) that protects critical visual cues from temporal decay; and a heavyweight Slow Reasoning Module that remains dormant during normal streams and is awakened only by suspicious events for semantic verification and causal description. Extensive experiments on multiple benchmarks demonstrate that ReactVAU operates under strict streaming constraints while simultaneously achieving competitive performance in both anomaly detection and causal reasoning, alongside significantly enhanced computational efficiency by minimizing heavyweight MLLM invocations. Project page is available at https://huiyuiui.github.io/React_VAU/