Feb. 8, 2024, 5:47 a.m. | Liyun Zhu Arjun Raj Lei Wang

cs.CV updates on arXiv.org arxiv.org

Industry surveillance is widely applicable in sectors like retail, manufacturing, education, and smart cities, each presenting unique anomalies requiring specialized detection. However, adapting anomaly detection models to novel viewpoints within the same scenario poses challenges. Extending these models to entirely new scenarios necessitates retraining or fine-tuning, a process that can be time consuming. To address these challenges, we propose the Scenario-Adaptive Anomaly Detection (SA2D) method, leveraging the few-shot learning framework for faster adaptation of pre-trained models to new concepts. Despite …

anomaly anomaly detection challenges cities cs.cv dataset detection education fine-tuning industry manufacturing novel presenting process retail retraining smart smart cities surveillance

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