## 4 Experiments
### Ablation Study
#### 4.5.3 The Effect of Edge Detection Module  
The edge detection module plays a role in guiding the prediction of parsing. It can separate the body part from contour information. The model B + T + E denotes concatenating both the GTM and edge detection, which yields about 2.3% improvement in terms of mIoU compared to the baseline model. This gain is mainly due to the accurate prediction at the boundary area between sematic parts. By introducing the edge information, the improvement is close to 3% mIoU for some categories with similar or adjacent appearances, such as socks and pants to the experimental data in Table 5. These results highlight that the edge detection module can further optimize the challenge of prediction accuracy especially in some small regions and unclear edge contours categories.  
\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} \hline \hline
**Method** & **Big** & **Hat** & **Hair** & **Glove** & **Glass** & **u-Cloth** & **Dress** & **Cost** & **Sacks** & **Pants** & **j-Solits** & **Seart** & **Skiat** & **Face** & **l-Arm** & **r-Arm** & **l-Leg** & **r-Leg** & **r-Shee** & **mIoU** \\ \hline B + T + S4 & 87.09 & 66.45 & 71.96 & 41.14 & 28.25 & 69.38 & 35.28 & 56.71 & 47.93 & 74.84 & 32.8 & 20.07 & 27.38 & 74.53 & 63.78 & 66.26 & 57.51 & 57.27 & 44.95 & 45.39 & 53.48 \\ B + T + S4 & 87.63 & 65.68 & 77.77 & 39.90 & 28.95 & 69.19 & 34.66 & 55.48 & 47.31 & 74.73 & 34.22 & 21.06 & 28.40 & 74.38 & 64.19 & 66.77 & 57.90 & 57.58 & 44.48 & 45.20 & 53.47 \\ B + T + E & 87.66 & 68.48 & 77.72 & 47.22 & 30.81 & 69.95 & 36.62 & 55.90 & 49.46 & 74.97 & 31.50 & 17.56 & 22.71 & 74.83 & 64.97 & 67.57 & 58.45 & 44.55 & 45.34 \\ B + T + E + D & 87.86 & 67.15 & 71.70 & 43.45 & 32.40 & 70.08 & 38.10 & 56.59 & 50.08 & 74.80 & 31.50 & 17.50 & 27.21 & 74.83 & 64.97 & 67.57 & 58.79 & 58.36 & 44.55 & 45.43 & 54.14 \\ (nullary) & 87.57 & 67.28 & 72.11 & 42.24 & 33.70 & 70.46 & 37.82 & 57.07 & 50.55 & 75.21 & 32.16 & 17.67 & 28.73 & 74.84 & 65.54 & 67.83 & 59.60 & 58.81 & 45.28 & 45.42 & 54.50 \\ (lons) & & & & & & & & & & & & & & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of different components in DFE module.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline
**Method** & **Big** & **Hat** & **Hair** & **Glove** & **Glass** & **u-Cloth** & **Dress** & **Cost** & **Sacks** & **Pants** & **j-Solits** & **Seart** & **Skiat** & **Face** & **l-Arm** & **r-Arm** & **l-Leg** & **r-Leg** & **r-Shee** & **mIoU** \\ \hline B + T + S4 & 87.09 & 66.45 & 71.96 & 41.14 & 28.25 & 69.38 & 35.28 & 56.71 & 47.93 & 74.84 & 32.8 & 20.07 & 27.38 & 74.53 & 63.78 & 66.26 & 57.51 & 57.27 & 44.95 & 45.39 & 53.48 \\ B + T + S4 + C4 & 87.63 & 65.68 & 77.77 & 39.90 & 28.95 & 69.19 & 34.66 & 55.48 & 47.31 & 74.73 & 34.22 & 21.06 & 28.40 & 74.38 & 64.19 & 66.77 & 57.90 & 57.58 & 44.48 & 45.20 & 53.47 \\ B + T + E + D & 87.66 & 68.48 & 77.72 & 47.22 & 30.81 & 69.95 & 36.62 & 55.90 & 49.46 & 74.97 & 31.50 & 17.50 & 27.21 & 74.83 & 64.97 & 67.57 & 58.45 & 45.42 & 55.34 \\ B + T + E + D & 87.66 & 67.15 & 71.70 & 43.45 & 32.40 & 70.08 & 38.10 & 56.59 & 50.08 & 74.80 & 31.50 & 17.50 & 27.21 & 74.83 & 64.97 & 67.57 & 58.79 & 58.36 & 44.55 & 45.43 & 54.14 \\ (nullary) & 87.57 & 67.28 & 72.11 & 42.24 & 33.70 & 70.46 & 37.82 & 57.07 & 50.55 & 75.21 & 32.16 & 17.67 & 28.73 & 74.84 & 65.54 & 67.83 & 59.60 & 58.81 & 45.28 & 45.42 & 54.50 \\ (lons) & & & & & & & & & & & & & & & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of per-class IoU on the LIP validation set. “B” means baseline module. “T” means GTM: “E”, “D” denote edge detection module and DFE module.