# Tamed Warping Network for High-Resolution Semantic Video Segmentation
## 5 Experiments
### Results on the CamVid Dataset  
We also conduct experiments on the CamVid dataset, which contains 367, 100, and 233 video clips for training, validation, and testing, respectively, at a resolution of 720 \(\times\) 960. We apply the same configurations as those of Cityscapes except for the crop size. As shown in Tables 10 and 11, TWNet achieves consistent results on CamVid. ### Results on the CamVid Dataset  
We also conduct experiments on the CamVid dataset, which contains 367, 100, and 233 video clips for training, validation, and testing, respectively, at a resolution of 720 \(\times\) 960. We apply the same configurations as those of Cityscapes except for the crop size. As shown in Tables 10 and 11, TWNet achieves consistent results on CamVid. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Warping Layer** & **FT** & **CFR** & **RGA** & **mIoU \(\uparrow\)** & **FPS \(\uparrow\)** \\ \hline \hline \multirow{4}{*}{Layer 1} & ✓ & & & 68.8 & 183.1 \\  & ✓ & & & 69.9 & **183.1** \\  & ✓ & ✓ & & 71.0 & 179.8 \\  & ✓ & ✓ & ✓ & **71.5** & 175.2 \\ \hline \hline \multirow{4}{*}{Layer 2} & ✓ & & & 66.7 & 252.6 \\  & ✓ & & & 68.1 & **252.6** \\  & ✓ & ✓ & & 69.3 & 245.8 \\ \cline{1-1}  & ✓ & ✓ & ✓ & **70.0** & 240.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Effect of each module on the CamVid test set. \(\uparrow\): higher is better. The best results are shown in bold. ✓ indicates the module is used. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Model** & **Eval Set** & **Resolution** & **mIoU-pf \(\uparrow\)** & **mIoU \(\uparrow\)** & **FPS-pf \(\uparrow\)** & **FPS \(\uparrow\)** & **FPS \(\uparrow\)** & **Params** & **(M) \(\downarrow\)** & **GPU** \\ \hline _Per-frame Models_ & & & & & & & & & \\ ICNet [22] & val & \(1024\times 2048\) & 67.7 & - & 30.3 & - & 49.7 & 25.17 & TITAN \\  & & & & & & & & & \\ ERFNet [27] & test & \(1024\times 2048\) & 69.7 & - & 11.2 & - & 18.4 & 2.08 & TITAN \\  & & & & & & & & & X(M) \\ SwiftNetRN-18 [19] & val & \(1024\times 2048\) & 74.4 & - & 34.0 & - & 34.0 & 12.9 & 1080 Ti \\ CAS [21] & val & \(1024\times 2048\) & 74.0 & - & 34.2 & - & 48.9 & 1070 & \\ Liu et al. [23] & val & \(1024\times 2048\) & 73.9 & - & 20.8 & - & 20.8 & 3.2 & 1080 Ti \\ TD-SP18 [25] & val & \(1024\times 2048\) & 76.8 & - & 11.8 & - & 10.5 & 12.77 & Titan \\ DABNet [29] & test & \(512\times 1024\) & 70.1 & - & **104.0** & - & **104.0** & 0.76 & 1080 Ti \\ LRNNet [58] & test & \(512\times 1024\) & 72.2 & - & 71.0 & - & 71.0 & 0.68 & 1080 Ti \\ LEANet [14] & test & \(512\times 1024\) & 71.9 & - & 77.3 & - & 77.3 & 0.74 & 1080 Ti \\ LAANet [13] & test & \(512\times 1024\) & 73.6 & - & 95.8 & - & 95.8 & **0.67** & 1080 Ti \\ DDRNet-23-slim [46] & test & \(1024\times 2048\) & 77.4 & - & 101.6 & - & 79.37 & 5.7 & 2080 Ti \\ \hline _Video-based Models_ & & & & & & & & & \\ DFF [34] & val & \(512\times 1024\) & 71.1 & 69.2 & 1.52 & 5.6 & 12.8 & N/A & K40 \\ DVSNet1 [33] & val & \(1024\times 2048\) & 73.5 & 63.2 & 5.6 & 30.4 & 30.4 & 42.73 & 1080 Ti \\ DVSNet2 [33] & val & \(1024\times 2048\) & 73.5 & 70.4 & 5.6 & 19.8 & 19.8 & 62.9 & 1080 Ti \\ Prop-mv [31] & val & \(1024\times 2048\) & 75.2 & 61.7 & 1.3 & 7.6 & 9.6 & 8.7 & Tesla \\  & & & & & & & & K80 \\ Interp-mv [31] & val & \(1024\times 2048\) & 75.2 & 66.6 & 1.3 & 7.2 & 9.1 & 8.7 & K80 \\ Low-Latency [32] & val & \(1024\times 2048\) & **80.2** & **75.9** & 2.8 & 8.4 & - & 50.1 & N/A \\ LMA [59] & val & \(512\times 1024\) & 72.1 & 73.7 & 99.0 & **86.2** & 67.2 & N/A & 2080 Ti \\ \hline _Ours_ & & & & & & & & \\ TWNet-Layer1 & val & \(1024\times 2048\) & 73.6 & 71.6 & 35.5 & 61.8 & 61.8 & 12.35 & \\ TWNet-Layer1 & test & \(1024\times 2048\) & 73.1 & 71.2 & 35.5 & 61.8 & 61.8 & 12.35 & \\ TWNet-Layer2 & val & \(1024\times 2048\) & 73.6 & 69.5 & 35.5 & 84.9 & 84.9 & 12.14 & \\ TWNet-Layer2 & test & \(1024\times 2048\) & 73.1 & 69.0 & 35.5 & 84.9 & 84.9 & 12.14 & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of SOTA models on Cityscapes.