# Tamed Warping Network for High-Resolution Semantic Video Segmentation
## 6 Conclusions  
We present a novel fast framework TWNet for high-resolution semantic video segmentation. TWNet is based on warping, but unlike previous warping-based methods, it adds a correction stage after warping to alleviate the errors incurred by warping. Technically, we build two core models: KFC and NKFC. KFC is a key-frame CNN, which is used to perform segmentation for key frames and to send the features to the following non-key frame. NKFC is a non-key-frame CNN, which is used to extract the spatial details of the current non-key frame and perform warping based on the features of the preceding frame. Both spatial features and warped features of the current non-key frame are sent to the next correction stage. We propose two efficient modules for the correction stage, namely, CFR and RGA, to correct the warped features by learning the feature-space residuals. The experimental results demonstrate that our method is generic to the backbone choices and flow model choices. Our model is much more robust than previous warping-based approaches, and it maintains high speed.  
This research received no external funding.  
Not applicable.  
The Cityscapes Dataset can be found at [[https://www.cityscapes-dataset.com/](https://www.cityscapes-dataset.com/)]([https://www.cityscapes-dataset.com/](https://www.cityscapes-dataset.com/)). The CamVid Dataset can be found at [[http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)]([http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Model** & **Resolution** & **mIoU-pf \(\uparrow\)** & **mIoU \(\uparrow\)** & **FPS-pf \(\uparrow\)** & **FPS \(\uparrow\)** & **FPS Norm \(\uparrow\)** & **Params (M) \(\downarrow\)** & **GPU** \\ \hline \hline \multicolumn{10}{l}{_Per-frame Models_} \\ DFANet A [9] & 720 \(\times\) 960 & 64.7 & - & 120 & - & 196.8 & 7.8 & TITAN X \\ ICNet [22] & 720 \(\times\) 960 & 67.1 & - & 27.8 & - & 45.6 & 25.17 & TITAN X \\ BiSeNet [10] & 720 \(\times\) 960 & 68.7 & - & 116.2 & - & 103.4 & 13.43 & Titan Xp \\ BiSeNet V2 [43] & 720 \(\times\) 960 & 73.2 & - & 32.7 & - & 32.7 & 49 & 1080 Ti \\ Liu et al. [23] & 720 \(\times\) 960 & **78.2** & - & 27.8 & - & 27.8 & 3.2 & 1080 Ti \\ TD-PSP18 [25] & 720 \(\times\) 960 & 72.6 & - & 25 & - & 22.3 & 12.77 & Titan Xp \\ DABNet [29] & 360 \(\times\) 480 & 66.7 & - & **124.4** & - & 124.4 & 0.76 & 1080 Ti \\ LRNNet [58] & 360 \(\times\) 480 & 67.6 & - & 83.0 & - & 83.0 & **0.67** & 1080 Ti \\ LEANet [14] & 360 \(\times\) 480 & 67.5 & - & 98.6 & - & 98.6 & 0.74 & 1080 Ti \\ LAANet [13] & 360 \(\times\) 480 & 67.9 & - & 112.5 & - & 112.5 & **0.67** & 1080 Ti \\ DRRNet-23-slim [46] & 720 \(\times\) 960 & 74.3 & - & 230 & - & 179.7 & 5.7 & 2080 Ti \\ \hline \multicolumn{10}{l}{_Video-based Models_} \\ Prov-mv [31] & 720 \(\times\) 960 & 68.6 & 63.4 & 3.6 & 21.4 & 27.0 & 8.7 & Tesla K80 \\ Interp-mv [31] & 720 \(\times\) 960 & 68.6 & 67.3 & 3.6 & 19.1 & 24.1 & 8.7 & Tesla K80 \\ \hline \multicolumn{10}{l}{_Ours_} \\ TWNet-Layer1 & 720 \(\times\) 960 & 73.5 & **71.5** & 103.5 & 175.2 & 175.2 & 12.35 & 1080 Ti \\ TWNet-Layer2 & 720 \(\times\) 960 & 73.5 & 70.0 & 103.5 & **240.7** & **240.7** & 12.14 & 1080 Ti \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison with others on the CamVid test set. \(\uparrow\): higher is better; \(\downarrow\): lower is better. The best results are shown in bold.