# Towards Feasible Capsule Network for Vision Tasks
## 5 Conclusions  
This study introduces a pioneering hybrid architecture for capsule networks, seamlessly integrating a pre-trained backbone model with a dedicated capsule head tailored to the task. Through extensive experimentation, our approach demonstrates superior  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Study** & **Accuracy (\%)** \\ \hline \multirow{8}{*}{CIFAR 10} & HitNet [20] & 73.3 \\  & Two-phase routing [37] & 75.82 \\  & KDE Routing [49] & 84.6 \\  & DCNET++ [34] & 89.32 \\  & Self-Routing [16] & 92.14 \\  & DeepCaps [52] & 92.74 \\  & DE-CapsNet [35] & 92.96 \\  & Encapsulation [24] & 95.45 \\  & **CapsHead (ours)** & **94.97** \\ CIFAR 100 & Encapsulation [24] & 73.33 \\  & **CapsHead (ours)** & **79.91** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of CapsHead compared to prior works.  
performance in a range of vision tasks, delivering particularly notable advancements in pixel-level tasks like segmentation.  
However, it is important to acknowledge the limitations of our study. While our hybrid architecture showcases promising results, further optimization is needed to address its computational complexity, which could hinder its scalability to larger and more intricate datasets. Additionally, the trade-offs between performance gains and potential increased training times should be explored in more depth. Furthermore, we did not explicitly demonstrate the equivariance property of the proposed architecture, both theoretically and practically. While the potential for equivariance exists within our framework, a comprehensive theoretical exploration within the representation learning context is a direction we intend to pursue in future studies.  
Conceptualization, L.B.T.A.; methodology, L.B.T.A., D.T.V. and J.Y.K.; software, D.T.V.; validation, G.H.Y., L.B.T.A. and D.T.V.; formal analysis, D.T.V.; investigation, L.B.T.A. and G.H.Y.; resources, L.B.T.A.; data curation, G.H.Y.; writing--original draft preparation, L.B.T.A.; writing--review and editing, L.B.T.A. and G.H.Y.; visualization, D.T.V.; supervision, J.Y.K.; project administration, G.H.Y. and J.Y.K.; funding acquisition, G.H.Y., J.Y.K. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (no. 2021-0-02068, Artificial Intelligence Innovation Hub) and the MSIT (Ministry of Science and ICT), Korea, under the Innovative Human Resource Development for Local Intellectualization support program (IITP-2023-RS-2022-00156287) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).  
Not applicable.  
The CIFAR10 and CIFAR100 datasets are published 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 1 March 2023), the VOC2012 dataset is published at [[http://host.robots.ox.ac.uk/pascal/VOC](http://host.robots.ox.ac.uk/pascal/VOC)]([http://host.robots.ox.ac.uk/pascal/VOC](http://host.robots.ox.ac.uk/pascal/VOC)) (accessed on 1 March 2023), the Lung-CT-Scan dataset can be downloaded from [[https://www.kaggle.com/code/travishong/image-segmentation-for-lung-ct](https://www.kaggle.com/code/travishong/image-segmentation-for-lung-ct)]([https://www.kaggle.com/code/travishong/image-segmentation-for-lung-ct](https://www.kaggle.com/code/travishong/image-segmentation-for-lung-ct)) (accessed on 2 February 2023).  
We extend our heartfelt gratitude to [PERSON] and [PERSON] at SEO Corporation, Gyeonggi-do, Republic of Korea. Without their invaluable contributions, this study would not have reached its current state. Their roles spanned from managing funding to advising on content. We deeply appreciate their dedication and significant contributions to our work.  
The authors declare no conflict of interest.