# 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  
We also conduct experiments on the CACD dataset, which is a widely used cross-age face dataset containing a large number of celebrity face images ranging from 14 to 62 years old. To comprehensively evaluate the performance of our method, we also use three evaluation metrics: MAE, CS(3) and CS(5), for comparison with other methods. Detailed comparisons are made among non-hierarchical and hierarchical methods to demonstrate the effectiveness of our proposed model on the CACD dataset. The experimental results on the CACD dataset are reported in Table 3. Our method achieves an MAE of 4.47, significantly outperforming most competing methods. Among non-hierarchical methods, Soft softmax [44] reaches an MAE of 5.19, dLDLF [45] reaches 6.16, DEX [32] reaches 6.52, CasCNN [46] reaches 5.23 and RNDF [47] reaches 4.59; all of them are clearly higher in MAE than our method. Meanwhile, our model also demonstrates strong competitiveness among hierarchical methods. ADPF [28] achieves an MAE of 5.39 and MoE [14] achieves 5.19, but our model achieves a lower MAE of 4.47, outperforming these hierarchical methods. Additionally, our model also excels in cumulative scores, achieving 52.91% for CS(3) and 77.63% for CS(5), both of which are significantly higher than other methods. Though GroupFace [20] achieves the lowest MAE, this should be attributed to its complex multi-hop graph attention and group-aware margin coupling technology. These comparative results clearly demonstrate the superiority of our method on the CACD dataset, showing its ability to better handle age estimation tasks for cross-age faces. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Method**} & **Year** & **Pretrained** & **MAE** & **CS(3)** & **CS(5)** \\ \hline Non-Hierarchical & MobileNet [15] & 2018 & IMDB-WIKI & & 44.00 & 60.60 \\  & DenseNet [15] & 2018 & IMDB-WIKI & - & 51.70 & 69.40 \\  & LRN [43] & 2020 & IMDB-WIKI & 2.15 & 62.86 & 81.47 \\  & DAA [9] & 2023 & IMDB-Wiki & 2.25 & 68.82 & 84.89 \\ Hierarchical & Posterior [34] & 2017 & IMDB-WIKI & - & 62.08 & 80.43 \\  & Posterior [34] & 2017 & MS-Celeb & - & 64.23 & 82.15 \\  & SSR-Net [15] & 2018 & IMDB-WIKI & - & 54.90 & 74.10 \\  & ALD-Net [38] & 2023 & - & - & 61.40 & 81.20 \\  & OURS & 2025 & ImageNet & 3.09 & 63.11 & 82.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison results for age estimation on MegaAge-Asian. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Method**} & **Year** & **Pretrained** & **MAE** & **CS(3)** & **CS(5)** \\ \hline Non-Hierarchical & Soft softmax [44] & 2015 & IMDB-WIKI & 5.19 & - & - \\  & dLDLF [45] & 2017 & - & 6.16 & 50.50 & 71.10 \\  & DEX [32] & 2018 & - & 6.52 & 48.50 & 68.20 \\  & CasCNN [46] & 2018 & - & 5.23 & 36.20 & 56.30 \\  & RNDF [47] & 2019 & IMDB-WIKI & 4.59 & - & - \\  & DRF [48] & 2019 & IMDB-WIKI & 5.63 & 51.40 & 72.80 \\ Hierarchical & ADPF [28] & 2022 & IMDB-WIKI & 5.39 & - & - \\  & ALD-Net [38] & 2023 & - & 4.62 & - & - \\  & J-MIMO [40] & 2023 & ImageNet & 4.78 & - & - \\  & GroupFace [20] & 2024 & IMDB-WIKI & 4.32 & - & - \\  & MoE [14] & 2024 & IMDB-WIKI & 5.19 & 48.50 & 70.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results for age estimation on CACD. The first row reports the baseline performance when only the Swin Transformer is employed for global age estimation, achieving 2.33 MAE and 72.99% CS(3) on Morph II, 3.18 MAE and 61.98% CS(3) on MegaAge-Asian and 5.15 MAE and 42.91% CS(3) on CACD, which already provides a solid starting point thanks to the strong representation power of Swin Transformer. In the second row, the inclusion of the local adjustment module refines the global prediction, the MAE drops slightly to 2.31 and CS(3) increases to 74.53% on Morph II, while the MAE improves to 3.12 but CS(3) falls marginally to 60.23% on MegaAge-Asian. On CACD, the inclusion of local adjustment reduces the MAE to 5.02 and increases CS(3) to 43.80%.