## 4 Experiments
### Qualitative Comparison  
We provide the quality results on the LIP dataset in Figure 7. Compared with CE2P, our results are more reasonable. For example, in Figure 7b, the coat and upper-clothes are easily confused. There are inaccurate boundaries between adjacent parts of the body resolution and many easily confused categories, such as the coat and upper-clothes in the second column; in the fifth column, long skirts with similar appearances to short skirts are easily misidentified. Benefitting from the GTM, our method can correctly predict them. With the help of DFE, we also observe from the last row that the model can learn the detail information and accurately identifies the left/right arm and areas obscured by objects. Consequently, our method can obtain reasonable and precise results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Pixel Acc.** & **Mean Acc.** & **mIoU** \\ \hline DeepLab [9] & 82.66 & 51.64 & 41.64 \\ Attention [37] & 83.43 & 54.39 & 42.92 \\ DeepLab (ResNet-101) & 84.09 & 55.62 & 44.80 \\ MuLA [14] & 88.50 & 60.50 & 49.30 \\ JPPNet [13] & 86.39 & 62.32 & 51.37 \\ CE2P [6] & 87.37 & 63.20 & 53.10 \\ \hline Ours & 87.66 & 66.09 & 54.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different methods on the validation set of the LIP dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c} \hline \hline
**Method** & **Big** & **Hat** & **Hat** & **Hair** & **Clave** & **Glas** & **u-Cloth** & **Dress** & **Catat** & **Socks** & **Pants** & **j-Suits** & **Scorf** & **Skiat** & **Face** & **L-Amm** & **r-Amm** & **l4.4g** & **r-Leg** & **l-Shoe** & **r-Shee** & **mIoU** \\ \hline SegNet[5] & 70.02 & 28.60 & 44.01 & 0.01 & 0.00 & 34.46 & 0.00 & 15.97 & 3.99 & 33.56 & 0.01 & 0.00 & 0.00 & 52.38 & 15.30 & 24.23 & 13.82 & 13.17 & 9.26 & 6.47 & 18.17 \\ Depth[4] & 83.25 & 57.94 & 6.11 & 28.08 & 18.40 & 60.94 & 23.17 & 47.03 & 55.41 & 60.40 & 22.38 & 14.29 & 18.54 & 62.07 & 49.44 & 51.66 & 67.47 & 34.60 & 28.22 & 22.41 & 41.64 \\ Attention [20] & 84.00 & 58.87 & 66.78 & 23.32 & 19.48 & 63.20 & 29.63 & 49.70 & 35.23 & 66.04 & 24.73 & 19.84 & 23.04 & 70.58 & 50.27 & 54.03 & 89.35 & 37.00 & 26.20 & 27.09 & 42.92 \\ DeepLab[1] & 84.09 & 59.76 & 66.22 & 28.70 & 23.91 & 64.95 & 33.68 & 52.86 & 37.87 & 60.48 & 26.15 & 17.44 & 25.23 & 70.00 & 54.22 & 53.89 & 39.06 & 38.27 & 26.95 & 28.36 & 48.80 \\ IPNet[31] & 86.26 & 63.55 & 70.20 & 36.16 & 24.86 & 68.15 & 31.42 & 55.65 & 44.56 & 72.19 & 28.59 & 28.39 & 25.41 & 73.36 & 63.97 & 63.88 & 86.21 & 57.99 & 44.02 & 44.09 & 51.37 \\ C2 EP1 [3] & 87.67 & 65.29 & 72.54 & 39.09 & 32.73 & 69.46 & 32.52 & 56.28 & 64.97 & 47.11 & 27.23 & 14.19 & 22.51 & 75.50 & 63.14 & 66.99 & 60.10 & 58.59 & 46.63 & 46.12 & 53.10 \\ \hline Ours & 85.87 & 67.28 & 72.11 & 42.24 & 33.70 & 74.46 & 37.82 & 57.67 & 50.55 & 75.21 & 32.16 & 17.57 & 28.73 & 74.84 & 65.54 & 67.83 & 59.40 & 58.81 & 45.28 & 45.42 & 54.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison in terms of mean pixel Intersection-over-Union (mIoU) with state-of-the-art methods on LIP validation set. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Pixel Acc.** & **F.G.Acc** & **Avg.P.** & **Avg.R.** & **Avg.F-1** \\ \hline ATR [33] & 91.11 & 71.04 & 71.69 & 60.25 & 64.38 \\ DeepLab [9] & 94.42 & 82.93 & 78.48 & 69.24 & 73.53 \\ PSPNet [11] & 95.20 & 80.23 & 79.66 & 73.79 & 75.84 \\ Attention [37] & 95.41 & 85.71 & 81.30 & 73.55 & 77.23 \\ Co-CNN [7] & 96.02 & 83.57 & 84.95 & 77.66 & 80.14 \\ \hline Ours & 96.10 & 84.02 & 84.87 & 80.89 & 80.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different methods on the ATR test dataset. Figure 7: Visualization of different methods on the LIP validation dataset.