# Gaze Estimation via Strip Pooling and Multi-Criss-Cross Attention Networks
## 4 Experiment
### Ablation Studies
When both modules are adopted, the mean angular error is 10.13\({}^{\circ}\) and 6.61\({}^{\circ}\) on each dataset, respectively. The final experiments show that adopting both modules in the backbone network at the same time produces better model performance and improves gaze estimation. The ablation experimental analyses in Table 5 all adopt smooth L1 loss. We also conducted a comparative analysis of the proposed SPMCCA-Net and L2 CS-Net via feature map visualization, as depicted in Figure 10. Figure 10 shows the original test map from the Gaze360 dataset, while Figure 10 shows feature map fusion visualization results produced by the L2 CS-Net model. The feature maps generated by the original network contain a lot of redundant information, making it difficult to locate the eye regions. Figure 10 shows feature map fusion visualization results for SPMCCA-Net when adopting both proposed modules. Redundant information in the feature maps that is unrelated to gaze is substantially reduced, and the model is more sensitive to feature information from the eye regions. This model assigns greater weight values to those regions while assigning lower weight values to gaze-independent regions, which largely prevents the influence of gaze-independent information on gaze estimation. \begin{table}
\begin{tabular}{c c c c} \hline
**Image Resolution** & **Methods** & **Gaze360** & **RT-Gene** \\ \hline
448 \(\times\) 448 & L2 CS-Net & 10.41\({}^{\circ}\) & 6.68\({}^{\circ}\) \\
448 \(\times\) 448 & SPMCCA-Net & **10.13\({}^{\circ}\)** & **6.61\({}^{\circ}\)** \\
224 \(\times\) 224 & L2 CS-Net & 10.62\({}^{\circ}\) & 6.77\({}^{\circ}\) \\
224 \(\times\) 224 & SPMCCA-Net & **10.33\({}^{\circ}\)** & **6.68\({}^{\circ}\)** \\
112 \(\times\) 112 & L2 CS-Net & 11.68\({}^{\circ}\) & 7.32\({}^{\circ}\) \\
112 \(\times\) 112 & SPMCCA-Net & **11.28\({}^{\circ}\)** & **7.08\({}^{\circ}\)** \\ \hline \end{tabular}
\end{table} 
Table 4: Ablation analysis of different input image resolutions. \begin{table}
\begin{tabular}{c c c c} \hline
**Methods** & **Module** & **Gaze360** & **RT-Gene** \\ \hline L2 CS-Net (\(\beta\) = 1) (baseline) & - & 10.47\({}^{\circ}\) & 6.69\({}^{\circ}\) \\ L2 CS-Net (\(\beta\) = 2) (baseline) & - & 10.33\({}^{\circ}\) & 6.65\({}^{\circ}\) \\ SPMCCA-Net (\(\beta\) = 1) & SP & 10.22\({}^{\circ}\) & 6.64\({}^{\circ}\) \\ SPMCCA-Net (\(\beta\) = 2) & SP & 10.21\({}^{\circ}\) & 6.63\({}^{\circ}\) \\ SPMCCA-Net (\(\beta\) = 1) & MCCA & 10.28\({}^{\circ}\) & 6.65\({}^{\circ}\) \\ SPMCCA-Net (\(\beta\)=2) & MCCA & 10.26\({}^{\circ}\) & 6.63\({}^{\circ}\) \\ \hline
**SPMCCA-Net (\(\beta\) = 1)** & **SP + MCCA** & **10.16\({}^{\circ}\)** & **6.63\({}^{\circ}\)** \\
**SPMCCA-Net (\(\beta\) = 2)** & **SP + MCCA** & **10.13\({}^{\circ}\)** & **6.61\({}^{\circ}\)** \\ \hline \end{tabular}
\end{table} 
Table 5: Analysis of ablation experiments of the two modules. Since the base method pays more attention to the design of the loss structure aspect than the feature extraction part, it can be seen from Figures 9 and 10 that simply improving the loss function part can also be very effective in improving the model convergence speed and the accuracy of the gaze estimation, while in the feature extraction part, although the degree of attention to the eye regions and the anti-interference ability of redundant information are greatly improved, there is still a tendency to further improve and reduce the final mean angular error. We additionally conducted ablation analyses for various positions of the two modules applied to the Gaze360 dataset. In Table 6, A indicates that only bottleneck blocks from the final layer of ResNet-50 are adopted as SPbottleneck blocks, while B indicates that the final bottleneck blocks in every layer are adopted as SPbottleneck blocks, and C indicates that all bottleneck blocks are adopted as SPbottleneck blocks. None of the ablation experimental analyses in Table 6 are incorporated into the multi-criss-cross attention module, and all of them adopted smooth L1 loss.