Traditional approaches for video analysis often misdefine anomalies; they usually rely on single-modality input and have inadequate management of complex temporal patterns. This paper resolves these ...

Industrial anomaly detection is hindered by data inefficiency and dependence on large-scale training sets. We introduce CLIP-FSQAE, a novel framework for few-shot anomaly detection that integrates ...

Introduction: Recent advances in artificial intelligence have created opportunities for medical anomaly detection through multimodal learning frameworks. However, traditional systems struggle to ...