{"type":"ScholarlyArticle","title":"MemoVAD: Resource-Efficient Video Anomaly Detection via Dynamic Semantic Memory in Edge Computing Scenarios","authors":[{"name":"Guo Li","slug":"guo-li","given":"Guo","family":"Li","affiliation":""},{"name":"Jiandian Zeng","slug":"jiandian-zeng","given":"Jiandian","family":"Zeng","affiliation":""},{"name":"Yang Li","slug":"yang-li","given":"Yang","family":"Li","affiliation":""},{"name":"Zihao Peng","slug":"zihao-peng","given":"Zihao","family":"Peng","affiliation":""},{"name":"Ke Chen","slug":"ke-chen","given":"Ke","family":"Chen","affiliation":""},{"name":"Tian Wang","slug":"tian-wang","given":"Tian","family":"Wang","affiliation":""}],"abstract":"Deploying Video Anomaly Detection (VAD) in real-world surveillance faces a fundamental tension between the demand for high-level semantics to ensure effectiveness and the limited computational resources of edge devices. Vision–Language Models (VLMs) provide rich open-vocabulary semantics, but their latency and computational cost preclude on-device deployment. To address the challenge, we propose MemoVAD, an edge–cloud collaborative framework that selectively incorporates VLM semantics into streaming VAD. MemoVAD runs most inference on the edge with a lightweight detector and a causal Temporal Context Encoder (TCE) to model temporal dependencies. Specifically, we introduce an Uncertainty-Aware Gating (UAG) policy grounded in Subjective Logic to model perceived uncertainty and query the cloud-based VLM only for high-uncertainty and semantically novel clips. Besides, a Dynamic Semantic Memory (DSM) is designed to cache VLM-verified prototypes for efficient retrieval, enabling the edge model to progressively incorporate VLM-level semantics via a semantic adapter. Experiments on UCF-Crime and XD-Violence datasets via a real edge device show that MemoVAD substantially reduces communication overhead while surpassing state-of-the-art performance. The demo video is available at: https://memovad2026.github.io/.","doi":"10.24963/ijcai.2026/618","url":"https://ijcai.org/proceedings/2026/618","pdf":"https://ijcai.org/proceedings/2026/0618.pdf","markdown":"https://ijcai.org/proceedings/2026/618.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/618","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":618,"pages":{"first":5549,"last":5557},"keywords":["Multidisciplinary Topics and Applications: Ubiquitous computing cystems","Multidisciplinary Topics and Applications: Real-time systems","Multidisciplinary Topics and Applications: Sensor networks and smart cities"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}