# A Self-Paced Multiple Instance Learning Framework for Weakly Supervised Video Anomaly Detection
## 4 Experiments
### Comparison with the Prior SOTA  
In this section, we primarily compare the performance of our proposed method DeepMIL+SP-MIL, RTFM+SP-MIL and UR-DMU+SP-MIL with other state-of-the-art methods. Results on UCF-Crime. Table 6 shows that UR-DMU+SP-MIL achieved competitive results. For comparison, [PERSON] et al. [28] focused on addressing the inter-video data imbalance problem by proposing an adversarial and focus training method. However, their performance was 2.69% lower than that of UR-DMU+SP-MIL. Similarly, [PERSON] et al. [11] introduced a method that focuses on pseudo-label completeness and uncertainty, employing Monte Carlo dropout to select confident video clips and enhance classifier performance. However, their results were slightly lower than those of the RTFM+SP-MIL method. In addition, our UR-DMU+SP-MIL method is comparable to that of [PERSON] et al. [29], who incorporated additional textual information to enhance the performance of their model. Compared to MIST [9], which utilized a two-stage training strategy to improve the accuracy for pseudo-labels, the UR-DMU+SP-MIL method not only outperformed it by 5.46% but also demonstrated its versatility by improving the capabilities of classifiers with various baseline models. Results on ShanghaiTech. Table 7 presents the AUC results of different WS-VAD methods on the ShanghaiTech dataset. It can be observed that RTFM+SP-MIL achieves an AUC of 98.10%. This may be attributed to the fact that our framework can enhance the classifier's discriminability through self-paced learning strategy. Compared to the noisy label cleaning \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(\eta\) & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.8** & **0.9** \\ \hline AUC (\%) & 83.94 & 84.60 & 84.68 & 85.40 & 85.50 & 85.49 & 85.41 & 84.80 & 83.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study on parameter \(\eta\) on the UCF-Crime dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(R\) & **3** & **5** & **8** & **10** & **15** & **20** & **25** & **30** & **32** \\ \hline AUC (\%) & 84.43 & 84.68 & 85.07 & 85.50 & 84.23 & 83.67 & 82.99 & 81.24 & 80.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation study on parameter \(R\) on the UCF-Crime dataset. strategy in GCN [1], our model demonstrates an AUC improvement of 13.66%, highlighting the benefit of refining segment-level pseudo-labels via the SP-MIL framework. Additionally, [PERSON] et al. [30] and [PERSON] et al. [16] improved the temporal modeling of pre-trained features. However, their performance remains slightly lower than that of our RTFM+SP-MIL method. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Source** & **Feature** & **AUC (\%)** \\ \hline DeepMIL [7] & CVPR 2018 & C3D RGB & 75.41 \\ DeepMIL * [7] & CVPR 2018 & I3D RGB & 78.32 \\ DeepMIL * [7] & CVPR 2018 & CLIP & 80.91 \\ GCN [1] & CVPR 2019 & TSN RGB & 82.12 \\ CLAWS [31] & ECCV 2020 & C3D RGB & 83.03 \\ MIST [9] & CVPR 2021 & I3D RGB & 82.30 \\ RTFM [5] & ICCV 2021 & I3D RGB & 84.30 \\ RTFM * [5] & ICCV 2021 & I3D RGB & 83.50 \\ RTFM * [5] & ICCV 2021 & CLIP & 84.45 \\ [PERSON] et al. [14] & TMM 2021 & I3D RGB & 84.62 \\ [PERSON] et al. [16] & TIP 2021 & I3D RGB & 84.89 \\ BN-SVP [32] & CVPR 2022 & I3D RGB & 83.39 \\ TCA-VAD [33] & ICME 2022 & I3D RGB & 83.75 \\ MSE [6] & AAAI 2022 & I3D RGB & 85.30 \\ [PERSON] et al. [34] & PRA 2023 & I3D RGB & 83.56 \\ [PERSON] et al. [13] & TNNLS 2023 & I3D RGB & 85.42 \\ [PERSON] et al. [35] & ICME 2023 & I3D RGB & 85.88 \\ [PERSON] et al. [36] & CVPR 2023 & I3D RGB & 86.10 \\ [PERSON] et al. [11] & CVPR 2023 & I3D RGB & 86.22 \\ [PERSON] et al. [28] & PRA 2024 & I3D RGB & 85.07 \\ [PERSON] et al. [37] & WACV 2024 & I3D RGB+FLOW & 85.47 \\ [PERSON] et al. [29] & CVPR 2024 & CLIP (RGB+Text) & 87.79 \\ \hline DeepMIL+SP-MIL & I3D+CLIP & 84.81 \\ RTFM+SP-MIL & I3D+CLIP & 86.35 \\ UR-DMU+SP-MIL & I3D+CLIP & 87.76 \\ \hline \hline \end{tabular}  
* means that we re-implemented this method.  
\end{table}