# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
## 4 Result and Analysis
### Model Comparison  
Table 2 represents the comparative analysis of six different models, including Scenario Adaptive Anomaly Detection (SAAD) [1; 27], Robust Temporal Feature Magnitude (RTFM), Magnitude-Contrastive Glance and Focus Network (MGFN), Uncertainty Regulated Dual Memory Unit (UR-DMU), Text Empowered Video Anomaly Detection (TEVAD) [27], and the anomaly detector model (MLP + MAML + SWIN). The MLP + MAML + SWIN model outperformed all other models in comparison due to its unique combination of meta-learning and advanced feature extraction. Here, the SAAD model adopted a reconstruction-based few-shot learning strategy using U-Net and ConvLSTM to predict future frames and score anomalies via PSNR. RTFM and MGFN are representative weakly supervised methods that rely on pre-extracted features using I3D and swin and multi-instance learning-based objectives, focusing on identifying abnormal segments from video-level labels. Similarly, UR-DMU was designed to model normal behavior representations effectively while simultaneously extracting distinctive features from abnormal data, enabling a more accurate differentiation between normal and anomalous states. By integrating both visual and textual information, TEVAD enhances spatio-temporal features with semantic insights, allowing for more meaningful interpretation of abnormal events. All RTFM, MGFN, UR-DMU, and TEVAD models used swinT and I3D as their backbone architecture for anomaly detection.  
Unlike traditional models like SAAD, which achieved a lower AUC of 0.69, or RFTM, MGFN, UR-DMU, and TEVAD, which reached AUCs of 0.867, 0.85, 0.72, and 0.83, respectively, the implemented model achieved a remarkable AUC of 0.91, where the anomaly detector model distinguished itself by combining swin transformer for robust spatial feature extraction with a meta-learned MLP classifier trained using MAML. In this model, the five-shot one-query approach was adopted for episodic training setup, and a ten-shot one-query approach was adopted for testing setup, enabling strong adaptation across diverse scenarios and camera views. This few-shot classification-based formulation allowed for efficient learning with limited examples and achieved superior AUC scores compared to all other evaluated methods, highlighting the strength of combining a powerful visual backbones with meta-learning in a classification-driven anomaly detection pipeline. This significant improvement is largely attributed to the integration of MAML, which allows the model to adapt quickly to new scenarios with minimal data, a crucial feature in video anomaly detection, where anomalies vary across different contexts. The swin transformer backbone enhanced feature extraction by effectively capturing spatial dependencies in the video frames through its attention mechanism. This enabled the model to accurately  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **AUC** \\ \hline SAAD & 0.69 \\ RTFM & 0.867 \\ MGFN & 0.85 \\ UR-DMU & 0.72 \\ TEVAD & 0.83 \\ MLP + MAML + SWIN (our model) & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model comparison.  
identify even subtle anomalies in complex video data. Furthermore, the combination of MLP with MAML and swin transformer leveraged the strengths of both powerful feature extraction and robust decision making, ensuring optimal performance.