MemoVAD: Resource-Efficient Video Anomaly Detection via Dynamic Semantic Memory in Edge Computing Scenarios
Researchers have developed MemoVAD, a novel framework for resource-efficient video anomaly detection on edge devices. This system uses a combination of edge and cloud processing, with a unique uncertainty-aware gating policy that only sends high-uncertainty clips to a cloud-based Vision-Language Model. A dynamic semantic memory stores VLM-verified prototypes, allowing the edge model to progressively learn richer semantics and significantly reduce communication overhead while maintaining high performance. AI
IMPACT Introduces a method to integrate advanced VLM semantics into edge devices for anomaly detection, reducing latency and communication costs.