# MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection
## 5 Conclusions  
In this paper, we proposed MT-CMVAD, a novel Multi-modal Transformer framework for cross-modal video anomaly detection, which addresses the critical challenges of ineffective cross-modal fusion and limited long-range dependency modeling. By integrating dynamic cross-modal attention, hierarchical spatiotemporal interaction, and adaptive anomaly scoring mechanisms, our approach achieves robust and interpretable anomaly detection in complex video scenarios. Extensive experiments on three benchmark datasets validate the superiority of our method. Specifically, MT-CMVAD attains state-of-the-art AUC scores of 98.9% on UCF-Crime, 94.7% on UBI-Fights, and 82.9% on the synthetic UBnormal dataset, outperforming existing methods such as DMAD (65.1% on UBnormal) and ICCBCB (94.1% on UBI-Fights). These results highlight the effectiveness of our cross-modal fusion strategy in capturing \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Resolution** & **AUC** &
\begin{tabular}{c} **Avg.** \\ **InferSpeed** \\ \end{tabular} & \(\Delta\)**AUC** & \(\Delta\)**Avg. InferSpeed** \\ \hline
224 \(\times\) 144 & 95.7 & 2.89 & \(-\)3.2\% & \(-\)15.4\% \\
256 \(\times\) 176 & 97.5 & 3.07 & \(-\)1.4\% & \(-\)10.2\% \\
288 \(\times\) 208 & 98.3 & 3.24 & \(-\)0.6\% & \(-\)5.2\% \\
320 \(\times\) 240 & 98.9 & 3.42 & - & - \\
336 \(\times\) 256 & 98.9 & 3.63 & +0.0\% & +6.1\% \\
352 \(\times\) 272 & 99.0 & 3.71 & +0.1\% & +8.4\% \\
384 \(\times\) 304 & 99.1 & 4.01 & +0.2\% & +17.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of Model Inference Performance on UCF-Crime Dataset at Different Resolutions (where Avg. InferSpeed stands for Average Inference Speed in seconds per video). Figure 7: Qualitative examples of anomaly detection results with model responses. fine-grained spatial-temporal correlations and semantic dependencies across modalities. Additionally, the computational optimizations, including dynamic resolution processing and sparse attention mechanisms, reduce memory consumption by 14.3% in FLOPs and accelerate convergence by 18.7% compared with conventional approaches, enhancing scalability for real-world deployment. Despite these advancements, there are still limitations in handling extreme occlusions and domain shifts across heterogeneous surveillance environments. Moreover, although our model has made certain progress in inference speed, there is still room for improvement in real-time inference capabilities. Future work will focus on integrating self-supervised learning to enhance generalization ability, exploring lightweight architectures for real-time processing, and further optimizing the model's inference speed to meet a broader range of real-time video surveillance needs. Our approach provides a promising foundation for advancing video surveillance, autonomous monitoring, and public safety applications through adaptive and efficient anomaly detection, while also highlighting the directions that require further research and refinement in practical applications. Conceptualization, H.D. and S.L. ; methodology, H.D. ; software, H.D. and S.L. ; validation, S.L. ; formal analysis, S.L. ; investigation, Y.C. ; resources, H.Y. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.