# Semantic Matching Based on Semantic Segmentation and Neighborhood Consensus
## 4 Experiments
### Qualitative Results  
In line with previous works, we used the keypoint-based PCK as the quantitative metric for evaluating semantic matching accuracy, but we still hope to qualitatively evaluate  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**PF-PASCAL (\(\alpha\) img)**} & \multicolumn{3}{c}{**PF-WILLOW (\(\alpha\) img)**} \\ \cline{2-7}  & \(\mathbf{\alpha=0.05}\) & \(\mathbf{\alpha=0.10}\) & \(\mathbf{\alpha=0.15}\) & \(\mathbf{\alpha=0.05}\) & \(\mathbf{\alpha=0.10}\) & \(\mathbf{\alpha=0.15}\) \\ \hline UCN-ST [8] & 0.299 & 0.556 & 0.740 & 0.241 & 0.540 & 0.665 \\ SCNet [35] & 0.362 & 0.722 & 0.820 & 0.386 & 0.704 & 0.853 \\ A2 Net [46] & 0.428 & 0.708 & 0.833 & 0.363 & 0.688 & 0.844 \\ CNNGeo [40] & 0.460 & 0.758 & 0.884 & 0.382 & 0.712 & 0.858 \\ SFNet [34] & - & 0.787 & - & - & 0.740 & - \\ Weakalign [47] & 0.490 & 0.748 & 0.840 & 0.370 & 0.702 & 0.799 \\ CAT-FOSS [22] & 0.336 & 0.689 & 0.792 & 0.362 & 0.546 & 0.692 \\ RTNs [45] & 0.552 & 0.759 & 0.852 & 0.413 & 0.719 & 0.862 \\ SAOLD [48] & 0.528 & 0.727 & 0.792 & - & - & - \\ NCNet [31] & 0.542 & 0.789 & 0.860 & 0.440 & 0.727 & 0.854 \\ \hline Ours & **0.555** & **0.842** & **0.932** & **0.454** & **0.747** & **0.863** \\ \hline \end{tabular}
\end{table}
Table 1: Evaluation results on PF-PASCAL and PF-WILLOW. The best average PCK scores are in bold. The data are sourced from [45] and the running results of source codes.  
the dense matching performance of our method. Therefore, we warped the image according to the estimated dense semantic matching field and analyzed the matching accuracy of all points according to the warping quality. Specifically, we warped the source image to make it semantically aligned with the target image. Ideally, the warped images and target images should have the same semantic content at the same position on the image. Figure 5 presents some warping examples based on our semantic matching method. It shows that in different scenarios, on the one hand, the object in the warped image is similar to the object in the target image, and on the other hand, the warped images are smooth with less distortion and artifacts. This demonstrates that in addition to the keypoints, the network can also establish good semantic correspondences for other points in the image.  
We visualized the keypoint estimation errors as shown by the color line segments in the first and fourth columns of Figure 5. In these source images, the dots represent ground-truth keypoints; the boxes represent the estimated keypoints by translating ground-truth keypoints from target images to the source according to the predicted semantic correspondence. Ideally, the dot and the box should coincide on the image, that is, they should have the same coordinates. However, due to the semantic matching error, there is a spatial deviation between them, as shown by the line segment between the dot and the box. The longer line segment means the greater error of semantic matching. Figure 5 shows that in different scenarios, the spatial deviations between the real and estimated keypoints of our method are small.  
Figure 5: Image warping and keypoint estimation visualization. The warped images are semantically aligned with the target images. The color line segments (in columns 1 and 4) depict semantic matching errors, where the dots represent ground-truth keypoints, while the boxes represent the estimated keypoints by keypoint transferring from the target image to the source based on the predicted semantic matching.