ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding
ReactVAU is a slow-fast streaming framework for real-time video anomaly understanding that reserves heavyweight MLLM reasoning for suspicious events, improving efficiency.
ReactVAU addresses causal streaming video anomaly understanding with three components: a lightweight Fast Detection Module using Spatial Grid Folding, Anomaly-Aware Persistent Memory that protects critical visual cues from temporal decay, and a Slow Reasoning Module activated only on suspicious events. This design minimizes heavyweight MLLM invocations during long normal intervals. Experiments show competitive anomaly detection and causal reasoning under strict streaming constraints with significantly enhanced computational efficiency.
- Fast Detection Module continuously filters anomalies without violating causality.
- Anomaly-Aware Persistent Memory prevents critical visual cues from temporal decay.
- Slow Reasoning Module dormant during normal streams, awakened by suspicious events.
- Competitive detection and causal reasoning performance under strict streaming constraints.
- AI models
- ReactVAU
From the original article
1 min read at huggingface.co
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.
By Chia-Hui Chen, Shih-Ying Yeh, Fu-En Yang · huggingface.co
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