# End-to-End Object Detection with Enhanced Positive Sample Filter
## 4 Experiments
### Ablation Study
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **AP** & **AP\({}_{\mathbf{50}}\)** & **AP\({}_{\mathbf{75}}\)** & **AP\({}_{\mathbf{5}}\)** & **AP\({}_{\mathbf{M}}\)** & **AP\({}_{\mathbf{L}}\)** \\ \hline (a) & 41.5 & 59.6 & 45.7 & 25.1 & 44.9 & 52.0 \\ (b) & 41.6 & 59.7 & 45.7 & 25.5 & 44.8 & 51.6 \\ (c) & 41.3 & 59.4 & 45.2 & 26.2 & 44.6 & 51.5 \\ (d) & 41.4 & 59.5 & 45.3 & 24.3 & 44.5 & 52.2 \\ (e) & 42.0 & 60.0 & 46.3 & 26.3 & 45.2 & 52.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of DsFE module with different architectures on COCO val2017 set. The (a)–(e) indexes in the first column denote corresponding architectures that are presented in Figure 4.  
Figure 4: The (**a–e**) are diagrams of alternative architectures of DsFE module. The ’reg subnet’, ’cls subnet’ and ’obj subnet’ mean the four convolutional layers on top of FPN feature maps in regression, classification and objectiveness branches, respectively. **Architectures of Disentangled Max Pooling Filter. ** We evaluated different architectures of DeMF and present the results in Table 9. Firstly, we attached a third max pooling stream that employs the same architecture as the second one and the performance declines severely. This may be because the extra third max pooling stream brings diffused regions higher values and makes the final feature representation less discriminative in foreground local regions. Moreover, we modified the 3D max pooling operation in the second max pooling stream to the 2D max pooling, which only cares about the feature in the current level. The performance is inferior to that of the 3D max pooling version, which reflects that the cross-level enhancement is effective since different levels may learn complementary information. **Different Kernel Sizes of Max Pooling in Disentangled Max Pooling Filter. ** We also evaluated different kernel sizes of the 3D max pooling operation in the second max pooling stream of DeMF. As shown in Table 10, a 3 \(\times\) 3 kernel obtains the highest performance. The phenomenon confirms that the secondary maximum value within a limited range of local areas is helpful for feature enhancement. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Kernel Size** & **AP** & **AP\({}_{\mathbf{50}}\)** & **AP\({}_{\mathbf{75}}\)** & **AP\({}_{\mathbf{S}}\)** & **AP\({}_{\mathbf{M}}\)** & **AP\({}_{\mathbf{L}}\)** \\ \hline
1x1 & 41.5 & 59.3 & 45.3 & 25.2 & 45.1 & 52.2 \\
3x3 & 42.0 & 60.0 & 46.3 & 26.3 & 45.2 & 52.8 \\
5x5 & 41.5 & 59.7 & 45.5 & 25.0 & 45.0 & 52.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of different kernel sizes of the 3D max pooling operation in the second max pooling stream of DeMF on COCO val2017 set. Figure 5: The (**a**) and (**b**) are diagrams of alternative architectures of objectiveness branch. The pink cubes represent convolutional layers. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Label Assignment** & **AP** & **AP\({}_{\mathbf{50}}\)** & **AP\({}_{\mathbf{75}}\)** & **AP\({}_{\mathbf{S}}\)** & **AP\({}_{\mathbf{M}}\)** & **AP\({}_{\mathbf{L}}\)** \\ \hline one-to-9 & 41.8 & 59.7 & 46.0 & 25.6 & 45.1 & 53.2 \\ one-to-16 & 42.0 & 60.0 & 46.3 & 26.3 & 45.2 & 52.8 \\ one-to-25 & 41.7 & 59.7 & 45.9 & 25.5 & 45.0 & 52.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of different settings of label assignment in objectiveness branch on COCO val2017 set. “One-to-K’ represents averagely assigning \(K\) number of positive samples to each GT instance.