# An Axial Compression Transformer for Efficient Human Pose Estimation
## 5 Discussion  
This paper demonstrates the higher efficiency of Transformer structures in focusing on global feature extraction. We propose ACPose, an efficient human pose estimation method that achieves significant reductions in model consumption without sacrificing accuracy, thereby addressing the balance between accuracy and speed in human pose estimation. Through the careful design of axial compression and Local Enhancement Modules, ACPose achieves competitive results on both the COCO and MPII datasets. The qualitative experiments show that our method can more accurately predict keypoint locations compared to other lightweight models when applied to human pose estimation. Furthermore, ablation studies were conducted to determine the optimal parameter settings for our model.  
Despite achieving outstanding performance on the public datasets, we encountered certain challenges during our research. Specifically, while reducing dimensionality within Transformer, effectively lowering complexity, the current method does not differentiate the contribution of various tokens to human keypoint prediction during compression. This results in a degree of accuracy loss. Therefore, addressing this issue and exploring more nuanced token-specific compression strategies will be the focus of our future work.  
Conceptualization, W.T. and H.Z.; methodology, W.T. and H.Z.; software, W.T.; validation, W.T. and H.Z.; formal analysis, W.T.; investigation, W.T.; resources, W.T.; data curation, W.T.; writing--original draft preparation, W.T.; writing--review and editing, W.T.; visualization, W.T.; supervision, H.Z.; project administration, H.Z. and X.S.; funding acquisition, H.Z. and X.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Zhejiang Sci-Tech University Research Startup Funding Project, grant number 11121731282202-01.  
Not applicable.  
The original data presented in the study are openly available in Github at [[https://github.com/KKTYZ/ACPose](https://github.com/KKTYZ/ACPose)]([https://github.com/KKTYZ/ACPose](https://github.com/KKTYZ/ACPose)) (accessed on 12 March 2025).  
We would like to express our sincere gratitude to [PERSON] for his invaluable guidance and support. We also thank the editor and the reviewers for their insightful comments and contributions to this paper.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **h** & **\#Params** & **GFLOPs** & **GFLOPs\({}^{T}\)** & **Mean** \\ \hline TokenPose-L/D6 & - & 21.49 M & 11.85 & 0.64 & 90.2 \\ ACPose-L/D6 & 36 & 21.96 M & 11.78 & 0.57 (\(-\)10.9\%) & 90.1 (\(-\)0.1\%) \\ ACPose-L/D6 & 24 & 21.73 M & 11.66 & 0.45 (\(-\)29.7\%) & 90.0 (\(-\)0.2\%) \\ ACPose-L/D6 & 12 & 21.66 M & 11.60 & 0.39 (\(-\)39.0\%) & 90.0 (\(-\)0.2\%) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of different compression degrees h on MPII datasets.  
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.