# Semantic Attention Guided Hierarchical Decision Network for Age Estimation
## 4 Experimental Results and Analysis
### Comparisions with State-of-the-Arts
#### 4.4.3 Experiments on CACD Dataset
This shows that narrowing the search space of age is often helpful for age estimation. The third row replaces the local adjustment module with the semantic attention mechanism. It yields 2.28 MAE and 74.37% CS(3) on Morph II, 3.20 MAE and 59.49% CS(3) on MegaAge-Asian and 5.11 MAE and 42.74% CS(3) on CACD, confirming that semantic attention enhances discrimination for age on Morph II, although its benefit is less pronounced on the MegaAge-Asian set and CACD. The fourth row combines both local adjustment and semantic attention, significantly boosting accuracy. It yields 2.25 MAE and 74.27% CS(3) on Morph II, 3.06 MAE and 61.12% CS(3) on MegaAge-Asian and 4.51 MAE and 47.73% CS(3) on CACD. The joint use of the two modules clearly outperforms either one alone in terms of MAE and CS on all datasets. Finally, the last row shows the performance of our full model, which integrates Swin Transformer, local adjustment, semantic attention, and hierarchical bilinear pooling (HBP). The addition of HBP allows for effective cross-layer feature interactions, enriching the local feature representation. This comprehensive framework achieves the best results, with an MAE of 2.18 and a CS(3) of 79.93% on the Morph II dataset, a competitive MAE of 3.09 and CS(3) of 63.11% on MegaAge-Asian and 4.47 MAE and 52.91% CS(3) on CACD. Compared with the baseline Swin Transformer, all metrics show significant improvements across the three datasets. Overall, these results clearly demonstrate that each module contributes positively to the age estimation task. The global estimation stage with Swin Transformer provides \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Method**} & \multicolumn{3}{c}{**Morph II**} & \multicolumn{2}{c}{**MegaAge-Asian**} & \multicolumn{2}{c}{**CACD**} \\ \hline
**Swin** & **Local** & **Semantic** & & & & & & \\
**Transformer** & **Adjusted** & **Attention** & **HBP** & **MAE** & **CS(3)** & **MAE** & **CS(3)** & **MAE** & **CS(3)** \\ \hline \(\surd\) & & & & 2.33 & 72.99 & 3.18 & 61.98 & 5.15 & 42.91 \\ \(\surd\) & \(\surd\) & & & 2.31 & 74.53 & 3.12 & 60.23 & 5.02 & 43.80 \\ \(\surd\) & & \(\surd\) & & 2.28 & 74.37 & 3.20 & 59.49 & 5.11 & 42.74 \\ \(\surd\) & \(\surd\) & \(\surd\) & & 2.25 & 74.27 & 3.06 & 61.12 & 4.51 & 47.73 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 2.18 & 79.93 & 3.09 & 63.11 & 4.47 & 52.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of role of each module in our framework. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Year** & **Pretrained** & **MAE** & **CS(3)** & **CS(5)** \\ \hline  & OURS & 2025 & ImageNet & 4.47 & 52.91 & 77.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._a rough but solid prediction of the true age. The local adjustment module effectively refines this prediction by focusing on a narrowed age range. The semantic attention mechanism further enhances performance by selecting more discriminative, age-sensitive facial regions for fine-grained local feature learning. Finally, HBP complements the pipeline by capturing complementary information across feature layers. Together, this hierarchical design significantly improves the accuracy and robustness of age estimation, validating the effectiveness of our two-stage framework.