# End-to-End Object Detection with Enhanced Positive Sample Filter
## 4 Experiments
### Ablation Study  
**Components of Enhanced Positive Sample Filter** To validate the effectiveness of our proposed EPSF, we conducted an ablation study on its components, i.e., DsFE and DeMF. Table 5 summarizes the results. The second line shows the baseline results without using DsFE and DeMF. We first built DeMF upon the baseline, where the input of DeMF is from the regression branch. It can be seen that DeMF can raise consistent performance gains across all metrics. Then, we further added DsFE and obtained further improvements. It can be seen that our entire end-to-end object detector achieves a significant performance gain over the baseline, which confirms the effectiveness of EPSF. **Architectures of Dual-stream Feature Enhancement Module** To validate the effectiveness of our proposed DsFE, we compared five alternative selection ways, whose corresponding diagrams are depicted in Figure 4. Table 6 summarizes the comparison results. From this table, one can observe that Figure 4e achieve the best performance by performing positive sample selection based on concatenated features from regression and objectiveness \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **NMS** & **R\(\downarrow\)** & **HO\(\downarrow\)** & **R + HO\(\downarrow\)** \\ \hline FasterRCNN + ATT [(42)] & ✓ & 10.3 & 45.2 & 18.2 \\ MS-CNN [(43)] & ✓ & 10.0 & 59.9 & 21.5 \\ RPN + BF [(44)] & ✓ & 9.6 & 74.4 & 24.0 \\ FasterRCNN [(45)] & ✓ & 9.2 & 57.6 & 20.0 \\ MGAN [(46)] & ✓ & 6.8 & 38.2 & 13.8 \\ FCOS [(3)] & ✓ & 6.9 & 34.1 & 14.2 \\ DeFCN [(12)] & \(\times\) & 7.1 & 34.4 & 14.3 \\ Ours & \(\times\) & 5.8 & 31.6 & 12.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparisons on Caltech test set. ‘NMS’ column indicates whether the method uses NMS. Numbers refer to mMR. Note that the lower mMR is better. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**DsFE** & **DeMF** & **AP** & **AP\({}_{50}\)** & **AP\({}_{75}\)** & **AP\({}_{S}\)** & **AP\({}_{M}\)** & **AP\({}_{L}\)** \\ \hline \(\times\) & \(\times\) & 39.1 & 56.2 & 42.9 & 24.4 & 42.5 & 49.6 \\ \(\times\) & ✓ & 41.5 & 59.6 & 45.7 & 25.1 & 44.9 & 52.0 \\ ✓ & ✓ & 42.0 & 60.0 & 46.3 & 26.3 & 45.2 & 52.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The effect of components of the proposed Enhanced Positive Sample Filter on COCO val2017 set. ‘DsFE’ and ‘DeMF’ indicate using the Dual-stream Feature Enhancement and Disentangled Max Pooling Filter respectively. branches, showing that merging both localization and objectiveness information provides the chance to produce many robust positive sample proposals. **Architectures of Objectiveness Branch** The objectiveness branch can be constructed in two ways as shown in Figure 5. We conducted experiments based on the two architectures and corresponding results are presented in Table 7. One can observe that constructing the objectiveness branch as an independent branch directly on top of FPN obtains a better performance than attaching it to the regression subnet. This is because their learning targets are different and the expected learned knowledge has large discrepancies. As a result, two independent branches perform the best. **Label Assignment in Objectiveness Branch. ** In order to learn good object representations via the objectiveness branch, a one-to-many label assignment strategy was employed. We tried to explore its best hyper-parameters, i.e., the average number of assigned positive samples. A larger number can provide more sufficient supervision to learn strong and robust representation, while a smaller number may facilitate the training of the proposed EPSF. As shown in Table 8, we find that averagely assigning 16 positive samples to each GT instance achieves the best overall performance.