# GLClick: Interactive Segmentation Combining Global and Local Features
## 4 Experiment
### Comparison with Other Methods
By intelligently analyzing and targeting the largest error regions in segmentation, GLClick achieves accurate results with minimal interaction cost, offering a more efficient and user-friendly solution for practical applications. Figure 6 provides detailed visualizations of the segmentation results from the GLClick model across four benchmark datasets, highlighting its significant advantages in handling  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Backbone**} & \multicolumn{2}{c}{**GrabCut**} & \multicolumn{2}{c}{**Berkeley**} & \multicolumn{2}{c}{**SBD**} & \multicolumn{2}{c}{**DAVIS**} & \multicolumn{2}{c}{**PascalVOC**} \\  & & **NoCS8** & **NoV0** & **NoCS8** & **NoV0** & **NoCS8** & **NoV0** & **NoCS8** & **NoC90** & **NoCS8** & **NoC90** \\ \hline DIGoS [8] & FCN  
complex image segmentation tasks. The comparisons reveal that the GLClick model significantly outperforms the baseline model across various challenging scenarios. For instance, in Figure 6c, where the cat's body is obscured by the owner's hand, and in Figure 6d, where the flamingo's thin legs present a segmentation challenge, the superior performance of the global-local fusion module (GLFM) is apparent. The GLFM module effectively integrates global contextual information with local detailed features, allowing the model to manage occlusions and accurately delineate objects. Global information provides insights into the overall scene layout and object positions, while local information captures the fine details of visible parts. As illustrated in Figure 6, GLClick consistently outperforms the baseline model in preserving object integrity and accurately delineating boundaries. This is particularly evident in scenarios involving fine structures and fuzzy edges, such as in the first row of Figure 6a, where GLClick accurately segments the target object without the common issues of over-segmentation or under-segmentation observed in baseline models. These results not only validate the effectiveness of the GLClick model in complex image segmentation tasks but also emphasize its significant advantages in improving segmentation accuracy and robustness. By consolidating these advantages, GLClick establishes itself as a robust and efficient framework for interactive image segmentation. Its global-local feature fusion module (GLFM) effectively combines the global context of transformers with the fine-grained details of CNNs, enabling superior performance across diverse datasets. GLClick significantly reduces user interaction by achieving high mIoU levels with fewer clicks, as demonstrated on benchmarks like DAVIS and Berkeley. This not only minimizes operational burden but also ensures precise segmentation in challenging scenarios, such as thin structures or occlusions. In Table 2, Our ViT-L-based GLClick model has 356.36 million parameters and 575.89 GFLOPs, which is relatively high compared to the other methods such as FocalClick (ViT-B: 96.46 M parameters, 169.78 GFLOPs). However, this increase in parameters is expected given the larger capacity of the ViT-L backbone, which enables the model to capture more  
Figure 6: Visualization results of the algorithm on four datasets. Positive and negative clicks are marked in green and red, respectively. The segmented region is shown in red. complex patterns and features from input images. The speed of our ViT-B-based model is 0.085 s per click, which is competitive when compared to other methods such as FocusCut (ResNet-50: 0.331 s per click) and SimpleClick (ViT-L: 0.086 s per click). The GLClick model with the ViT-L backbone has a slightly higher time of 0.125 s per click, which is still within an acceptable range for interactive segmentation tasks. The comparison indicates that GLClick achieves a good balance among accuracy, computational efficiency, and memory consumption.