This paper presents a concise review of video anomaly detection (VAD) and introduces a new dataset, Multi-Scenario Anomaly Detection (MSAD). The review highlights the critical relationship between model and dataset, emphasizing the importance of diverse and high-quality datasets for improving model performance. The authors identify practical issues, such as the lack of comprehensive datasets with diverse scenarios, and address this by creating MSAD, which includes 14 distinct scenarios and diverse motion patterns, including different lighting and weather conditions. The dataset is designed to provide a robust foundation for training superior models. The paper also introduces the SA²D model, which uses a few-shot learning framework to efficiently adapt to new concepts and scenarios. Experimental results demonstrate the model's superior performance in both cross-view and cross-scenario evaluations, showing its robustness and adaptability. The contributions of the paper offer valuable resources and insights to advance the field of VAD, addressing current challenges and setting the stage for future research directions.This paper presents a concise review of video anomaly detection (VAD) and introduces a new dataset, Multi-Scenario Anomaly Detection (MSAD). The review highlights the critical relationship between model and dataset, emphasizing the importance of diverse and high-quality datasets for improving model performance. The authors identify practical issues, such as the lack of comprehensive datasets with diverse scenarios, and address this by creating MSAD, which includes 14 distinct scenarios and diverse motion patterns, including different lighting and weather conditions. The dataset is designed to provide a robust foundation for training superior models. The paper also introduces the SA²D model, which uses a few-shot learning framework to efficiently adapt to new concepts and scenarios. Experimental results demonstrate the model's superior performance in both cross-view and cross-scenario evaluations, showing its robustness and adaptability. The contributions of the paper offer valuable resources and insights to advance the field of VAD, addressing current challenges and setting the stage for future research directions.