# GLClick: Interactive Segmentation Combining Global and Local Features
## 4 Experiment
### Ablation Studies  
Table 3 presents an ablation study that systematically evaluates the impact of different feature fusion strategies on image segmentation performance. The first row serves as the baseline, presenting segmentation results without any feature fusion methods, thus providing a reference for subsequent comparisons. The second row displays the results of integrating multi-layer features from ResNet50 and FPN using element-wise addition. While element-wise addition is a straightforward approach, it may not fully capture the complex relationships and interactions among features. As shown in the third row, the GLFM demonstrates exceptional performance. On the SBD dataset, the number of clicks required to achieve an IoU of 85% is only marginally higher by 0.02 clicks compared to the element-wise addition method; however, this slight difference does not detract from the overall effectiveness of the GLFM. The performance of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Backbone** & **Params/M** & **FLOPs/G** & **Mem/G** & **SPC/s** \\ \hline RITM [30] & HRNet-18s & 4.22 & 17.94 & 0.50 & 0.054 \\ RITM [30] & HRNet-18 & 10.03 & 30.99 & 0.52 & 0.056 \\ RITM [30] & HRNet-32 & 30.95 & 83.12 & 1.12 & 0.086 \\ FocalClick [26] & SegF-B0 & 3.72 & 3.42 & 0.10 & 0.037 \\ FocalClick [26] & SegF-B3 & 45.66 & 24.75 & 0.32 & 0.053 \\ CDNet [24] & ResNet-34 & 23.47 & 113.60 & 0.25 & 0.034 \\ MPC-Net [25] & ResNet-50 & 37.74 & 125.24 & 0.67 & 0.143 \\ Focuscut [23] & ResNet-50 & 40.36 & 78.82 & 0.85 & 0.331 \\ Focuscut [23] & ResNet-101 & 59.35 & 100.76 & 0.89 & 0.355 \\ SimpleClick [9] & ViT-B & 96.46 & 169.78 & 0.87 & 0.054 \\ SimpleClick [9] & ViT-L & 322.18 & 532.87 & 1.72 & 0.086 \\ Ours & ViT-B & 103.89 & 189.45 & 0.98 & 0.085 \\ Ours & ViT-L & 356.36 & 575.89 & 1.89 & 0.125 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Computation comparison of model parameters, FLOPs, GPU memory usage, and speed (seconds per click). Each method is identified by its backbone, with the input size specified (448 × 448 by default). Even with the ViT-H model, the speed (125 ms) and memory consumption (1.89 G) meet practical annotation tool requirements. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**GrabCut**} & \multicolumn{2}{c}{**Berkeley**} & \multicolumn{2}{c}{**SBD**} & \multicolumn{2}{c}{**DAVIS**} \\  & **85\%** & **90\%** & **85\%** & **90\%** & **85\%** & **90\%** & **85\%** & **90\%** \\ \hline Baseline & 1.40 & 1.54 & 1.44 & 2.46 & 3.28 & 5.24 & 4.10 & 5.48 \\ B+R & 1.36 & 1.42 & 1.37 & 2.23 & 3.07 & 4.98 & 3.90 & 5.24 \\ B+R+G & 1.34 & 1.42 & 1.36 & 2.15 & 3.09 & 4.95 & 3.88 & 5.06 \\ B+R+G+M & **1.30** & **1.42** & **1.36** & **2.13** & **3.01** & 5.02 & **3.67** & **5.01** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation experiments performed on the frozen baseline part. B: Baseline; R: Resnet50; G: GLFM; M: MLP; 85% and 90% signify the corresponding NoC metrics achieved at mIoU thresholds of 85% and 90%, respectively. The red font indicates the best results. the GLFM in other areas highlights its effective design, indicating that more refined feature fusion strategies can further enhance model performance. The fourth row introduces an enhanced multi-layer perceptron (MLP) module. This enhanced MLP module, with its advanced feature extraction and learning capabilities, significantly improves the model's ability to understand and utilize complex image features, resulting in notable performance gains across various evaluation metrics. The integration of the MLP module with the existing model achieved a breakthrough in performance, laying a solid foundation for future research and applications. In summary, the ablation study results indicate that while simple element-wise addition may be effective in some cases, the proposed GLFM module generally provides superior segmentation results, underscoring its substantial contributions to feature fusion and performance enhancement.