# Semantic-Aligned Cross-Modal Visual Grounding Network with Transformers
## 4 Experiments
### Experimental Results
#### 4.3.3 Comparison with the State-of-the-Art Approaches  
Finally, we compare our approach with several state-of-art approaches; Tables 3 and 4 show the comparison results. It is demonstrated that our model substantially and consistently outperforms the state-of-the-art methods on both datasets, with about 1 to 3 percentage improvements. First, DETR and Deformable DETR demonstrate poorer performances as compared to YOLO and R-CNN, this is because DETR adopts query point features to discriminate extremely similar objects, and these discriminative query points are difficult to be found sometimes. Second, YOLO and R-CNN only employ visual features to discriminate between different objects, and the performance significantly depends on the feature extraction network. When the network cannot explore the discriminative features, the prediction is wrong. In comparison, this impressive performance of our approach stems from two aspects: (1) SAC-VGNet introduces text descriptions that incorporate visual representations, and the proposed multi-scale cross-modal fusion module could effectively fuse them to capture the tiny differences between similar categories. (2) SAC-VGNet adopts text-driven contrastive learning to align both text and multi-modal features. As a result, the learned feature could better distinguish different categories. Table 5 demonstrates the detailed results in each category between YOLOv7 and SAC-VGNet. Among the 40 categories, our approach outperforms YOLOv7 in 37 categories, and is worse in 3 categories. For instance, the mAP on \"Su34\" is increased by 7.5% by our approach since the introduction of text information on \"Su34\" could help our model better distinguish from other categories. On the other hand, we also discover that SAC-VGNet results in a slight performance drop in \"B1\", \"Be200\", and \"Rafale\". We assume that these three categories have similar text descriptions with other visually similar categories and, thus, the introduction of text information cannot well distinguish them. Figure 6: Three examples to illustrate the heatmap results by SAC-VGNet without different components. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Resolution** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **mAP** \\ \hline DETR [19] & & 0.850 & 0.847 & 0.831 \\ YOLOv5 [41] & & 0.862 & 0.854 & 0.836 \\ Deformable DETR [35] & 640\(\times\) & 0.859 & 0.857 & 0.838 \\ Sparse R-CNN [36] & 640 & 0.873 & 0.865 & 0.842 \\ YOLOv7 [24] & & 0.881 & 0.877 & 0.851 \\ SAC-VCNet & & **0.926** & **0.922** & **0.885** \\ \hline DETR [19] & & 0.875 & 0.869 & 0.841 \\ YOLOv5 [41] & & 0.898 & 0.882 & 0.856 \\ Deformable DETR [35] & 1280\(\times\) & 0.889 & 0.880 & 0.849 \\ Sparse R-CNN [36] & 1280 & 0.903 & 0.892 & 0.870 \\ YOLOv7 [24] & & 0.911 & 0.903 & 0.875 \\ SAC-VGNet & & **0.935** & **0.927** & **0.902** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance comparison between SAC-VGNet and the advanced approaches on FAD. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Resolution** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **mAP** \\ \hline DETR [19] & & 0.831 & 0.776 & 0.739 \\ YOLOv5 [41] & & 0.840 & 0.789 & 0.750 \\ Deformable DETR [35] & 640\(\times\) & 0.835 & 0.787 & 0.742 \\ Sparse R-CNN [36] & 640 & 0.851 & 0.792 & 0.758 \\ YOLOv7 [24] & & 0.856 & 0.815 & 0.762 \\ SAC-VGNet & & **0.887** & **0.840** & **0.782** \\ \hline DETR [19] & & 0.871 & 0.835 & 0.783 \\ YOLOv5 [41] & & 0.883 & 0.846 & 0.811 \\ Deformable DETR [35] & 1280\(\times\) & 0.878 & 0.838 & 0.801 \\ Sparse R-CNN [36] & 1280 & 0.887 & 0.852 & 0.816 \\ YOLOv7 [24] & & 0.892 & 0.856 & 0.823 \\ SAC-VGNet & & **0.905** & **0.859** & **0.840** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance comparison between SAC-VGNet and the advanced approaches on MAD.