# A Self-Paced Multiple Instance Learning Framework for Weakly Supervised Video Anomaly Detection
## 4 Experiments
### The Effectiveness of the SP-MIL Based Classifier Enhancement Strategy  
In this section, we verify the general effectiveness of the SP-MIL based classfier enhancement strategy using various baseline models (DeepMIL [7], RTFM [5], UR-DMU [24]), and the generality across different feature extractors (I3D, CLIP).  
Tables 1 and 2 present the comparative results between the baseline models and the enhanced classifiers on the ShanghaiTech, UCF-Crime, and XD-Violence datasets, with DeepMIL, RTFM, and UR-DMU as the baseline models and I3D and CLIP as the feature extractors, respectively. From Table 1, it can be observed that when I3D is used as the feature extractor, the performance of three baseline models is significantly improved by further training with the SP-MIL framework. Specifically, DeepMIL+SP-MIL achieved performance gains of 4.36%, 4.33%, and 1.20% on the UCF-Crime, ShanghaiTech, and XD-Violence over DeepMIL. Similarly, RTFM+SP-MIL achieved improvements of 1.70%, 0.35%, and 2.74% over RTFM, and UR-DMU+SP-MIL demonstrated improvements of 1.51%, 0.22%, and 0.72% over UR-DMU on the same datasets. Table 2 exhibits a comparable trend based on the CLIP video features. Tables 1 and 2 demonstrate that the SP-MIL framework enables the classifier to initially focus on easily identifiable video segments and progressively tackle more challenging examples near the classification boundary. Consequently, the classifier gradually accumulates crucial prior knowledge for the challenging segments, thereby enhancing its discriminative capability.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Feature Extractor** & \begin{tabular}{c} **UCF-Crime** \\ **AUC (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **ShanghaiTech** \\ **AUC (\%)** \\ \end{tabular} &
\begin{tabular}{c} **XD-Violence** \\ **AP (\%)** \\ \end{tabular} \\ \hline DeepMIL * & I3D & 78.32 & 92.71 & 73.24 \\ DeepMIL+SP-MIL & I3D & 82.68 & 97.04 & 74.44 \\ RTFM * & I3D & 83.50 & 97.39 & 78.07 \\ RTFM+SP-MIL & I3D & 85.20 & 97.74 & 80.81 \\ UR-DMU * & I3D & 85.02 & 97.54 & 80.05 \\ UR-DMU+SP-MIL & I3D & 86.53 & 97.76 & 80.77 \\ \hline \hline \end{tabular}
* means that we re-implemented this method.  
\end{table}
Table 1: The effectiveness of the SP-MIL for different baseline models (I3D feature is used).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Feature Extractor** & \begin{tabular}{c} **UCF-Crime** \\ **AUC (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **ShanghaiTech** \\ **AUC (\%)** \\ \end{tabular} &
\begin{tabular}{c} **XD-Violence** \\ **AP (\%)** \\ \end{tabular} \\ \hline DeepMIL * & CLIP & 80.91 & 94.96 & 74.03 \\ DeepMIL+SP-MIL & CLIP & 84.14 & 97.54 & 75.28 \\ RTFM * & CLIP & 84.45 & 97.70 & 79.11 \\ RTFM+SP-MIL & CLIP & 85.50 & 97.94 & 81.52 \\ UR-DMU * & CLIP & 86.76 & 97.56 & 80.64 \\ UR-DMU+SP-MIL & CLIP & 87.13 & 97.90 & 81.24 \\ \hline \hline \end{tabular}
* means that we re-implemented this method.  
\end{table}
Table 2: The effectiveness of the SP-MIL for different baseline models (CLIP feature is used).  
To further confirm this observation, we use t-SNE to visualize the features extracted from the output of the second layer of the classifier in both the baseline models and the baselines+SP-MIL on the UCF-Crime dataset. The results are shown in Figure 4. It illustrates that classifier features trained by the three baseline models remain intermixed. However, after additional training with the SP-MIL framework, normal and abnormal video features become distinctly separated, demonstrating the effectiveness of the SP-MIL framework.