## 4 Experiments
### Ablation Studies
#### 4.4.2 Results and Discussion  
Table 4 shows the ablation detail, algorithm complexity, and model performance for different design elements. In this table, red text indicates a decrease in performance relative to the baseline, while green text indicates an improvement in performance relative to the baseline. \(\surd\)\(\surd\)\(\urd\) indicates that the current design element is considered in the model. On both the COCO and VOC datasets, the Slicing and DSConv methods exhibited poor performance with respect to various accuracy metrics. Although the DSConv method demonstrated a slight performance improvement compared to the Slicing method on the VOC dataset, it still has a large gap in accuracy between both Strided Conv and our method. This indicates that using only a single design element does not achieve good model performance. When considering both slicing and DSConv, SliceSamp achieved a slightly better \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Slicing**} & \multirow{2}{*}{**DSConv**} & \multirow{2}{*}{**+Up**} & \multirow{2}{*}{**Params**} & \multirow{2}{*}{**FLOPs**} & \multicolumn{4}{c}{**COCO**} & \multicolumn{4}{c}{**VOC**} \\ \cline{6-13}  & & & & & & & & & & & & & \\ \hline \begin{tabular}{c} Strided Conv \\ (baseline) \\ \end{tabular} & - & - & - & 7.28 M & 17.2 G & 62.18 & +8.60 & 51.91 & 32.05 & 79.71 & 71.16 & 78.50 & 51.75 \\ \begin{tabular}{c} Slice \\ \end{tabular} & - & - & 6.00 M & 14.5 G & 63.48 & +5.99 & 50.65 & 30.76 & 76.93 & 76.95 & 49.92 \\ \begin{tabular}{c} DSConv \\ **(ours)** \\ \end{tabular} & - & - & 6.00 M & 14.5 G & 63.49 & +5.78 & 50.45 & 30.96 & 78.85 & 70.15 & 76.54 & 50.22 \\ \begin{tabular}{c} DSConv \\ **(ours)** \\ \end{tabular} & - & \(\surd\) & - & 5.24 M & 13.0 G & 63.49 & +5.78 & 50.44 & 30.96 & 78.85 & 70.15 & 76.54 & 50.22 \\ \begin{tabular}{c} DSConv \\ **(ours)** \\ \end{tabular} & \(\surd\) & - & **6.03 M** & **14.7 G** & **62.89** & **87.89** & **22.59** & **88.88** & **88.88** & 71.90 & 78.71 & 33.85 \\ \begin{tabular}{c} **SliceUpsamp** \\ \end{tabular} & \(\surd\) & \(\surd\) & - & **6.03 M** & **14.7 G** & **62.98** & **88.79** & **22.59** & **88.88** & **88.88** & 71.90 & 78.71 & 33.85 \\ \begin{tabular}{c} **SliceUpsamp** \\ \end{tabular} & \(\surd\) & \(\surd\) & - & **6.03 M** & **64.03** & **60.19** & **64.27** & **61.27** & **62.03** & **61.20** & **61.20** \\ \begin{tabular}{c} **SliceUpsamp** \\ \end{tabular} & \(\surd\) & \(\surd\) & - & 6.06 M & 14.8 G & **65.03** & 63.55 & 51.72 & 32.45 & 79.70 & **79.29** & **79.29** & **83.95** \\ \
\begin{tabular}{c} **SliceConv3\(\times\)3** \\ \end{tabular} & \(\surd\) & \(\surd\) & - & 6.06 M & 14.8 G & 63.29 & -1.75 & -1.99 & (4.00) & -1.019 & **43.69** & **44.79** & **62.220** \\
\begin{tabular}{c} **SliceConv3\(\times\)3** \\ \end{tabular} & \(\surd\) & - & - & 15.0 M & 33.0 G & 67.64 & +8.37 & 54.08 & 48.0 & 78.46 & 50.22 & 53.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study on COCO and VOC benchmarks based on the YOLOv5 model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Model** & **Iteration** & **Batch Size** & **Image Size** & **Params** & **FLOPs** & **mIoU** & **mAcc** \\ \hline \begin{tabular}{c} Patch Merging \\ (baseline) \\ \end{tabular} & Swin Transformer & 160,000 & 8 & 224 & 59.94 M & 236.1 G & 21.99 & 30.75 \\ \begin{tabular}{c} Maxpool \\ Softpool \\ \end{tabular} & Swin Transformer & 160,000 & 8 & 224 & 58.78 M & 235.4 G & 23.08 & 32.50 \\ \begin{tabular}{c} Softpool \\ \end{tabular} & Swin Transformer & 160,000 & 8 & 224 & 58.78 M & 235.4 G & 22.08 & 30.87 \\ \
\begin{tabular}{c} Adapool \\ **SliceSamp (ours)** \\ \end{tabular} & Swin Transformer & 160,000 & 8 & 224 & 58.78 M & 235.4 G & 20.95 & 29.13 \\
\begin{tabular}{c} **SliceSamp (ours)** \\ \end{tabular} & Swin Transformer & 160,000 & 8 & 224 & 59.97 M & 236.1 G & **24.57** & **34.39** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The accuracy and computational cost of the models on ADE20K.