# Diverse Feature-Level Guidance Adjustments for Unsupervised Domain Adapative Object Detection
## 4 Experiments
### Ablation Study  
The ablation experiment was designed to investigate the unique facilitative effects of the DFGAs method in UDAOD. This study focused on evaluating module effectiveness on the Cityscapes and Foggy Cityscapes datasets. Each module was independently integrated into the codebase and its effectiveness was empirically validated.  
We employed the UDAOD model TIA [16], based on the Faster R-CNN framework, for our first ablation experiment. The experimental results are presented in Table 4. Model A was the baseline model (TIA), which had 42.3% mAP. When we incorporated a PMA module, mAP was improved by 0.9%. Model C, with the ATCA module, contributed an additional 0.7% mAP value. Finally, Model D was equipped with the PMA and ATCA modules and achieved 43.6% mAP. These improvements confirm the role of PMA loss in aligning feature distributions across domains, while our ATCA module effectively enhanced the classifier's focus on foreground information.  
To demonstrate the generality of our modules, we integrated them into another model, CRDA [8], as viewed in Table 5. Model E was the baseline model of CRDA, which achieved  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **PMA** & **ATCA** & **mAP** \\ \hline A & – & – & 42.3 \\ B & ✓ & – & 43.2 \\ C & – & ✓ & 43.0 \\ D & ✓ & ✓ & 43.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results (%) of ablation experiment on the baseline TIA.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **KITTI \(\rightarrow\) City (mAP)** & **City \(\rightarrow\) KITTI (mAP)** \\ \hline Faster R-CNN & 30.2 & 53.5 \\ DA Faster R-CNN & 38.5 & 64.1 \\ SWDA & 37.9 & 71.0 \\ SAP & 43.4 & 75.2 \\ ATF & 42.1 & 73.5 \\ MeGA & 43.0 & 75.5 \\ TIA & 44.0 & 75.9 \\ \hline
**DFGAs** & **59.5** & **87.6** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results (%) of DFGAs on KITTI \(\rightarrow\) Cityscapes and Cityscapes \(\rightarrow\) KITTI datasets compared with other methods.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **Sim10K \(\rightarrow\) City (mAP)** \\ \hline DA Faster R-CNN & 34.3 \\ TIA & 39.6 \\ SWDA & 40.1 \\ MAF & 41.1 \\ \hline
**DFGAs** & **41.6** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results (%) of DFGAs on Sim10K \(\rightarrow\) Cityscapes datasets compared with other methods.  
29.9% mAP. Obviously, our PMA improved the mAP by 2.2% in Model F and ATCA improved it by 1.9% mAP in Model G, respectively. In Model H, the modules together yielded 2.5% mAP improvements. These substantial mAP improvements reinforced the effectiveness of our method, which offered plug-and-play convenience and was effective in cross-domain object detection.  
To investigate the influence of the stride of cross-channel calculation in the PMA module on domain adaptative object detection accuracy, we conducted a series of experimental training and validations on the baseline TIA [16]. Experiments with strides of 1, 2, 8, 16, and 32 were conducted. The findings, depicted in Figure 4, revealed that the PMA module reached peak performance with a stride of 16.  
For our ATCA strategy, we assessed the effects of fixed and adaptative thresholds on sample division through comparative experiments. We trained the model with thresholds fixed at 0.5, 0.7, and 1 based on the baseline CRDA [8], and the results are presented in Figure 5. Ultimately, our adaptative threshold sample division strategy, ATCA, outperformed the fixed threshold approach clearly.  
Figure 4: mAP(%) varies with the stride of channels within the PMA module.  
Figure 5: mAP(%) varies with threshold within the ATCA module.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **PMA** & **ATCA** & **mAP** \\ \hline E & – & – & 29.9 \\ F & ✓ & – & 32.1 \\ G & – & ✓ & 31.8 \\ H & ✓ & ✓ & 32.4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results (%) of ablation experiment on the baseline CRDA.