# SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation
## 4 Experimental Results
### Results
#### 4.5.2 Soft-Gated vs. Shuffle-Gated Block  
We compared the performance of the existing encoder block (soft-gated block) with the proposed shuffle-gated block. The shuffle-gated block performed slightly lower than the soft-gated block; nonetheless, it maintained high accuracy with considerably fewer FLOPs.  
Tables 6 and 7 reveal that the small model in the MPII dataset experienced a 2.1% decrease in accuracy at [EMAIL_ADDRESS], while the large model had a 0.7% reduction. Similarly, on the COCO dataset, the precision dropped by 3.1% and 1.3% at AP50, respectively. However, the reduced parameters and FLOPs demonstrate that the encoder was effectively made more lightweight while minimizing the performance loss.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & **Shuffle-Gated Block** & **Multi-Dilation Block** & **Params** & **GFLOPs** & **[EMAIL_ADDRESS]** \\ \hline \multirow{4}{*}{SMS-Net-Small} & & & 6.23 M & 4.34 & 88.3 \\  & & ✓ & 2.80 M & 1.50 & 87.7 \\  & ✓ & & 3.24 M & 2.12 & 87.2 \\  & ✓ & ✓ & 0.38 M & 0.30 & 85.6 \\ \hline \multirow{4}{*}{SMS-Net-Large} & & & 12.3 M & 8.55 & 88.6 \\  & & ✓ & 5.61 M & 3.00 & 88.4 \\ \cline{1-1}  & ✓ & & 6.53 M & 4.11 & 87.9 \\ \cline{1-1}  & ✓ & ✓ & 0.63 M & 0.46 & 87.7 \\ \hline \end{tabular}
\end{table}
Table 6: Ablation study results using the MPII dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Model** & **Shuffle-Gated Block** & **Multi-Dilation Block** & **Params** & **GFLOPs** & **AP** & **AP50** & **AP75** & **AR** \\ \hline \multirow{4}{*}{SMS-Net-Small} & & & 6.23 M & 3.26 & 67.4 & 89.4 & 74.7 & 69.1 \\  & & ✓ & 2.87 M & 1.17 & 67.3 & 89.5 & 74.0 & 70.6 \\  & ✓ & & 3.24 M & 1.59 & 65.7 & 88.4 & 72.9 & 69.1 \\  & ✓ & ✓ & 0.38 M & 0.22 & 61.1 & 86.4 & 67.4 & 64.7 \\ \hline \multirow{4}{*}{SMS-Net-Large} & & & 12.3 M & 6.42 & 70.8 & 91.4 & 78.2 & 74.0 \\  & & ✓ & 5.61 M & 2.25 & 70.6 & 90.5 & 78.3 & 73.8 \\ \cline{1-1}  & ✓ & & 6.35 M & 3.09 & 69.6 & 90.5 & 77.1 & 72.7 \\ \cline{1-1}  & ✓ & ✓ & 0.63 M & 0.35 & 65.6 & 89.2 & 72.5 & 69.2 \\ \hline \end{tabular}
\end{table}
Table 7: Ablation study results using the COCO validation dataset (256 \(\times\) 192).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Model** & **Backbone** & **Params** & **GFLOPs** & **AP** & **AP50** & **AP75** & **AR** \\ \hline \multirow{4}{*}{SMS-Net-Small} & EfficientNet-B0 [19] & 0.38 M & 0.22 & 61.1 & 86.3 & 67.4 & 64.7 \\  & MobileNetV2 [22] & 0.31 M & 0.22 & 58.1 & 85.3 & 63.7 & 62.0 \\  & MobileNetV3 [23] & 0.26 M & 0.13 & 53.8 & 83.0 & 58.1 & 57.0 \\ \hline \multirow{4}{*}{SMS-Net-Large} & EfficientNet-B0 [19] & 0.63 M & 0.35 & 65.6 & 89.2 & 72.5 & 69.2 \\  & MobileNetV2 [22] & 0.56 M & 0.34 & 65.3 & 88.4 & 72.5 & 68.7 \\ \cline{1-1}  & MobileNetV3 [23] & 0.51 M & 0.25 & 60.9 & 87.2 & 67.3 & 64.6 \\ \hline \end{tabular}
\end{table}
Table 5: Ablation study results based on different backbones using the COCO validation dataset (256 \(\times\) 192).  
4.5.3. Decoder 3 \(\times\) 3 Convolution vs. Multi-Dilation Block  
We compared the results of applying the conventional 3 \(\times\) 3 convolution after upsampling in the decoder block (the stacked hourglass network) with those of replacing it with the multi-dilation block. Tables 6 and 7 reveal that the [EMAIL_ADDRESS] for the MPII dataset decreased by 1.6% and 0.2% for the small and large models, respectively. The precision at AP50 decreased by 2.0% and 1.3%, respectively, for the COCO dataset. However, the difference in the parameters and FLOPs demonstrates a reduction in computation while minimizing the performance decline.