# Multi-Dimensional Fusion Attention Mechanism with Vim-like Structure for Mobile Network Design
## 5 Conclusions  
This paper systematically explored attention mechanisms and introduced the multi-dimensional Vim-like attention (MDVA) mechanism, successfully integrating it into MobileNetV2 and ShuffleNetV2 backbones. The experiments demonstrated that MDVA significantly improved classification and detection accuracy compared to other attention mechanisms like SE, CBAM, and CA while maintaining a compact model size suitable for mobile devices with minimal additional computational overhead. According to the ablation experiment, simultaneous processing and fusion of the information of three dimensions can help extract the feature information to the greatest extent. The integration of MDVA into standard CNN architectures enhanced performance without significantly increasing model size. However, it has limitations, including a narrow scope of experiments and a lack of in-depth theoretical analysis. Future work would explore MDVA's integration into a broader range of architectures, apply it to diverse computer vision tasks, investigate hardware efficiency optimizations, and leverage automated mechanism search techniques to further improve and demonstrate its potential in real-world scenarios. Future research directions include exploring MDVA's integration into a broader range of architectures, applying it to diverse computer vision tasks, optimizing for hardware efficiency, conducting in-depth theoretical analyses, and leveraging automated mechanism search techniques. Additionally, the current modified model performs linear operations on the input's three dimensions and then combines them through another branch. In the future, we can explore pre-combination techniques and conduct a series of experiments to verify their efficacy in feature extraction or downstream image tasks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Setting** & **Datasets** & **\#.Param. (M)** & **FLOPs (M)** & **mAP (0.5:0.95)** \\ \hline YOLOv5s [48] & & 7.23 & 16.5 & 37.2 \\ +SE & & 7.27 & 16.6 & 37.5\({}_{+0.3}\) \\ +CBAM & COCO & 7.27 & 16.6 & 37.7\({}_{+0.5}\) \\ +CA & & 7.26 & 16.5 & 38.0\({}_{+0.6}\) \\ +MDVA & & 7.23 & 16.5 & 38.3\({}_{+1.1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results on MS COCO dataset.  
**Author Contributions**: Conceptualization, [PERSON]; methodology, [PERSON]; software, [PERSON]; validation, [PERSON]; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, R.Z.; project administration, R.Z.; funding acquisition, R.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key R&D Plan under grant number 2021 YFF0601200 and 2021 YFF0601204.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: You can find more information about the dataset at the following links: at [[https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)]([https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)) (accessed on 8 April 2009) and [[https://cocodataset.org/#download](https://cocodataset.org/#download)]([https://cocodataset.org/#download](https://cocodataset.org/#download)) (accessed on 21 February 2015).  
**Conflicts of Interest**: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.