# Single-Shot Global and Local Context Refinement Neural Network for Head Detection
## 4 Experiments and Analysis
### Ablation Analysis  
Considering that the output form and loss function of our model are the same as CenterNet [18], and the model architecture is similar, we adopt CenterNet as the baseline. Following the original CenterNet [18], we set the three hyper-parameters \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) to 1, 0.1, and 1, respectively, in Equation (4) and set \(\alpha\) and \(\beta\) to 1 and 2 in Equation (1). For fair comparison, we employ ResNet50 instead of Dla34 [39] as the backbone relative to the standard CenterNet. In particular, the upsampling operator of CenterNet(ResNet50) is three deconvolution layers. We use the same settings to achieve a fair comparison during the training stage. For instance, the initialization of the parameters is the same for all models, and the input size is \(512\times 512\). Table 1 presents the ablation experiment's outcomes. We can see that our GRM and LRM can each outperform the baseline model in terms of AP by 2.29% and 1.04%, respectively. On the Brainwash test dataset, using LRM and GRM simultaneously can even improve performance by 2.71% over the benchmark model. Figure 4 displays the precision recall (PR) curves of the ablation analysis of CRNet at IoU 0.5 on the Brainwash dataset. These results show that LRM and GRM can effectively improve the performance of the head detector. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & \multicolumn{2}{c}{**Methodology**} & \multirow{2}{*}{**AP (\%)**} \\ \cline{2-3} \cline{5-4}  & **GRM** & & **LRM** \\ \hline CenterNet (ResNet50) & & & 91.18 \\ \hline CRNet & ✓ & & 93.47 \\ \hline CRNet & & ✓ & 92.22 \\ \hline CRNet (proposed) & ✓ & ✓ & 93.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The ablation study of CRNet on the Brainwash test dataset. Additionally, we further compare the influence of the number of GRM blocks used in CRNet by successively adding GRM \(M_{5}\), \(M_{4}\), \(M_{3}\), and \(M_{2}\) on the CRNet without LRM. As indicated in Table 2, GRM is applied to all four stages for the best outcome. Another observation from Table 2 is that the absolute improvement of adding GRM on \(S_{5}\) is the smallest. One possible explanation is that although the representation ability is strong in the global context at the high stage, the spatial representation ability is weak due to the large receptive field. Furthermore, we display qualitative results of GRM on the Brainwash test set. We exploit Grad-CAM [40] to visualize the class activation map (CAM)[41] for SNL blocks from different GRMs in Figure 5. Input images and the CAMs of the \(M_{5}\), \(M_{4}\), \(M_{3}\), and \(M_{2}\) are displayed in order from left to right. The CAM will highlight the area that has more influence on the result. The greater the contribution of the area to the final result, the more the area will be highlighted. It can be clearly seen that the process of the global context of images is refined step by step. In Figure 5, from left to right, almost all head center points become clearer and more accurate. The ability of GRM to obtain the global context and gradually refine the global context by combining features from various resolutions is demonstrated by this process. It can be determined from the last row in the figure that \begin{table}
\begin{tabular}{l l} \hline \hline
**Position of GRM** & **AP (\%)** \\ \hline None & 91.18 \\ \hline \(S_{5}\) & 91.45 \\ \hline \(S_{5}+S_{4}\) & 92.36 \\ \hline \(S_{5}+S_{4}+S_{3}\) & 92.82 \\ \hline \(S_{5}+S_{4}+S_{3}+S_{2}\) & 93.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The influence of the number of GRM blocks on the Brainwash test dataset. Figure 4: The PR curves of ablation analysis at IoU 0.5 on the Brainwash dataset. even the completely occluded head (left bottom) can be recalled through the refined global context, which is nearly impossible using the local context. We also experiment with various configurations of dilation rates on our model with GRM to find an optimal combination of LRM.