# An Axial Compression Transformer for Efficient Human Pose Estimation
## 4 Results
### Qualitative Results  
Figure 4 presents the comparative analysis of our method, ACPose, and the TokenPose [18] method on the COCO dataset. As illustrated in the figure, ACPose demonstrates a more accurate inference of the human keypoint locations. The first image reveals that ACPose excels at identifying the keypoints in overlapping images and exhibits heightened sensitivity in recognizing smaller human targets within an image. This suggests that ACPose's Local Enhancement Module effectively enhances the recognition capability for fine-grained details within images. The second and third images showcase ACPose's superior accuracy in predicting the human keypoints even under occlusion conditions. In conclusion, ACPose consistently out-performs the other methods in terms of pose estimation accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **\#Params** & **GFLOPs\({}^{T}\)** & **Hea** & **Sho** & **Elb** & **Wri** & **Hip** & **Kne** & **Ank** & **Mean** \\ \hline SimpleBaseline-R50 & 34.0M & - & 96.4 & 95.3 & 89.0 & 83.2 & 88.4 & 84.0 & 79.6 & 88.5 \\ SimpleBaseline-R101 & 53.0M & - & 96.9 & 95.9 & 95.9 & 84.4 & 88.4 & 84.5 & 80.7 & 89.1 \\ SimpleBaseline-R152 & 53.0M & - & 97.0 & 95.9 & 90.0 & 85.0 & 89.2 & 85.3 & 81.3 & 89.6 \\ HRNetW32 & 28.5M & - & 96.9 & 96.0 & 90.6 & 85.8 & 88.7 & 86.6 & 82.6 & 90.1 \\ \hline TokenPose-L/D6 & 21.4M & 0.64 & 97.1 & 95.9 & **91.0** & 85.8 & **89.5** & 86.1 & **82.7** & **90.2** \\ ACPose-L/D6 (Ours) & 21.66M & **0.39 (\(-\)39.0\%)** & **97.1** & **96.1** & 90.9 & **86.1** & 88.9 & **86.1** & 81.6 & 90.0 (\(-\)0.2\%) \\ \hline \hline \end{tabular} \(GFLO\)Ps\({}^{T}\) represents the GFLOPs of the Transformer part. Bold indicates the optimal indicator.  
\end{table}
Table 3: Performance comparison between ACPose and mainstream models on MPII dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **\#Params** & **GFLOPs\({}^{T}\)** & **AP** & **AP\({}^{50}\)** & **AP\({}^{75}\)** & **AP\({}^{L}\)** & **AP\({}^{M}\)** & **AR** \\ \hline TransPose-R-A4 & 6.0M & 8.9 & 3.38 & 72.6 & 89.1 & 79.9 & 68.8 & 79.8 & 78.0 \\ TransPose-H-S & 8.0M & 10.2 & 4.88 & 74.2 & 89.6 & 80.8 & 70.6 & 81.0 & 79.5 \\ TransPose-H-A6 & 17.5M & 21.8 & 11.4 & 75.8 & 90.1 & 82.1 & 71.9 & 82.8 & 80.8 \\ TokenPose-B * & 13.5M & 5.7 & 1.29 & 75.6 & 89.8 & 81.4 & 71.3 & 81.4 & 80.0 \\ TokenPose-L/D6 * & 20.8M & 10.3 & 1.97 & _77.7_ & 90.0 & 81.8 & 71.8 & 82.4 & 80.4 \\ \hline ViTPose-Base & 86.0M & 18.6 & - & 75.8 & 90.7 & 83.2 & 78.4 & 68.7 & 81.1 \\ SHaRPose-Base & 93.9M & 17.1 & - & 75.5 & 90.6 & 82.3 & 82.2 & 72.2 & 80.8 \\ \hline ACPose-B * & 13.9M & 5.1 (\(-\)11\%) & 0.68 (\(-\)47.3\%) & 75.7 (+0.1\%) & 92.5 & 82.7 & 72.8 & 80.0 & 78.2 \\ ACPose-L/D6 * & 21.0M & 8.8 (\(-\)15\%) & 0.37 (\(-\)81.2\%) & 77.4 (\(-\)0.4\%) & 93.6 & 84.8 & 74.3 & 81.8 & 79.9 \\ ACPose-L/D24 * & 27.5M & 9.9 & 1.52 & _77.4_ & 93.6 & 83.8 & 74.4 & 81.9 & 79.9 \\ \hline \hline \end{tabular} \({}^{*}\) The asterisk indicates the usage of GTBox; \(GFLO\)Ps\({}^{T}\) represents the computational cost of the Transformer part.  
\end{table}
Table 2: Performance comparison between ACPose and mainstream models on COCO dataset.