# Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning
## 4 Experiments
### Ablation Study  
In this section, to demonstrate the effectiveness of these modules in our approach, we performed ablation studies on the NWPU-Crowd dataset with cross-domain scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **SH-B** & \multirow{2}{*}{**MSE**} & **MALL** & \multirow{2}{*}{**MSE**} & **UCSD** & \multirow{2}{*}{**MSE**} \\  & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** \\ \hline NoAdapt & 22.4 & 31.3 & 5.11 & 5.98 & 16.23 & 18.22 \\ SFCN [10] & **17.1** & **26.1** & 2.56 & 3.88 & 2.09 & 2.42 \\ Ours & 17.4 & 26.8 & **2.55** & **3.81** & **2.03** & **2.41** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results for our methods on three different real-world datasets. We compare with baseline without adaptation and SFCN [10] (state-of-art) [10].  
Figure 7: The comparison results between ground truth, baseline with no adaptation, SFCN [10], and our method.  
More concisely, we used FE to represent the adaptive domain-invariant features-extracting module, DE to represent the density-map estimator module based on dynamic-\(\beta\) MAML, and CM to represent the crowd-counting refined-mapper module. We utilized the different modules on the source domain GCC dataset and verified the performance on the target domain NWPU-Crowd dataset. As shown in Table 3, compared with the baseline, we obtained a significant improvement, using only adaptation. Our proposed FE method, similar to CSRNet with adaptation, improved performance and reduced the MAE 4.12 and MSE 4.15, respectively. When DE was used to perform the model generalization module for density-map estimation, the improvement was significant, with a 0.75 and 1.12 improvement compared with FE only. When applying the FE + DE + CM module, the improvement was 1.65 and 1.84. The results indicate that the domain-alignment processing and model generalization, through performing feature extraction and density evaluation, proved effective in cross-domain scenarios. Finally, Figure 8 shows the visualization results of the real-world dataset. We selected different crowd-volume photos for the results visualization.  
In real-world scenarios, perspective is generally that of cameras on the ground or of drones in the sky. Nevertheless, the domain-shift issue affects performance due to weather, illumination, rotation, and scale changes. Our proposed method mainly focuses  
Figure 8: Visualization results of adaptation from GCC to real-world dataset.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Method**} & **GCC - \textgreater{} NWPU-Crowd** & \\  & **MAE** & **MSE** \\ \hline CSRNet w/o Adapt & 86.12 & 148.32 \\ CSRNet w/Adapt & 45.84 & 91.12 \\ Ours w/FE & 41.72 & 80.97 \\ Ours w/DE & 40.97 & 78.85 \\ Ours w/FE + CM & 40.83 & 79.13 \\ Ours w/FE + DE + CM & 39.18 & 77.29 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation Study: The performance of baseline with and without adaptation and our approach in cross-domain scenarios.  
on alleviating the issue above. Taking camera scenes from different angles, we first utilize UE4 to generate labeled annotations and synthetic images; the perspective, in the latter, was similar to a real-world position. Then, we used an adaptive domain-invariant feature-extracting module to extract the domain-invariant feature layer as a pre-training model. Next, we train the meta-learning model by using only a few labeled data. Finally, the network predicts the number of crowds.