# SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation
## 5 Conclusions  
This study introduced a lightweight network suitable for HPE. The soft-gated skip connection network, a model with SOTA in HPE, demonstrated high performance by weighting important channels, but it still had high FLOPs compared to other lightweight models. We proposed a lightweight pose estimation model based on the sequentially stacked structure of the hourglass network to address this issue. Particularly, a shufflegated block was introduced to reduce the computational load and number of parameters during the feature extraction of the encoder of each hourglass network. Additionally, a multi-dilation block was used in the decoder to secure the receptive fields of various scales without increasing the computational load. The results of using two datasets (i.e., MPII and COCO) demonstrated that the proposed model achieved an improved balance between computational efficiency and performance compared to existing models in HPE.  
Our future research will focus on simplifying complex structures, such as the shufflegated and multi-dilation blocks, to enhance model efficiency and improve performance. Furthermore, collecting and using additional pose estimation datasets will accurately measure the model's generalizability, enabling its effectiveness in diverse environments and conditions.  
Additionally, we deployed SMS-Net on mobile devices, which demonstrated satisfactory performance. However, comprehensive optimization has not yet been performed. Future studies will involve refining the model for better efficiency on these devices, including conducting hardware benchmarks to evaluate its performance under real-world conditions. These research directions are expected to improve the model's usefulness across various fields by equipping it with better performance and generalization capabilities.  
Conceptualization, B.-D.L.; methodology, W.-J.N. and K.-R.M.; software, W.-J.N. and K.-R.M.; validation, W.-J.N., K.-R.M. and B.-D.L.; formal analysis, W.-J.N., K.-R.M. and B.-D.L.; investigation, W.-J.N., K.-R.M. and B.-D.L.; resources, B.-D.L.; data curation, W.-J.N. and K.-R.M.; writing--original draft, W.-J.N., K.-R.M. and B.-D.L.; writing--review and editing, W.-J.N., [PERSON]-R.M. and B.-D.L.; visualization, W.-J.N. and K.-R.M.; supervision, B.-D.L.; project administration, B.-D.L.; funding acquisition, B.-D.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Kyonggi University's Graduate Research Assistantship 2024. This work was also supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (No. 2020R1A6A1A03040583).  
The datasets used in this study are publicly available. The MPII dataset can be accessed at [[http://human-pose.mpi-inf.mpg.de/](http://human-pose.mpi-inf.mpg.de/)]([http://human-pose.mpi-inf.mpg.de/](http://human-pose.mpi-inf.mpg.de/)) (accessed on 4 November 2024), and the COCO dataset is accessible at [[https://cocodataset.org/#home](https://cocodataset.org/#home)]([https://cocodataset.org/#home](https://cocodataset.org/#home)) (accessed on 4 November 2024). No new data were created or analyzed in this study.