# Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification
## 4 Experiment
### Modified U_Net Performance  
At first, we perform experiments on public LIP dataset [36]. There are 20 semantic labels in LIP dataset: background, hat, hair, glove, sunglasses, upper clothes, dress, coat, socks, pants, jumpsuits, scarf, skirt, face, left-arm, right-arm, left-leg, right-leg, left-shoe, and right-shoe. We change the output num of the last layer in U-net architecture (Figure 3) from 4 to 20, to adopt it for the semantic segmentation tasks on LIP dataset. Our proposed method is compared with current state-of-the-art methods, including SegNet [37], FCN-8s [38], DeepLabV2 [39], Attention [40], DeepLabV2 + SSL [36], Attention + SSL [36] and standard U_Net [12]. From Table 2 it can be observed that standard U_Net network [12] outperforms the state-of-the-art networks on human semantic segmentation dataset LIP [36], our modified U_Net network outperforms the standard U_Net network by 0.23% at overall accuracy, 0.26% at mean accuracy and 0.35% at mean IoU index. We group the 19 semantic labels of LIP dataset into 3 labels: head (hat, hair, sunglasses, scarf, face), torso (glove, upper clothes, dress, coat, left-arm, right-arm) and lower-body (socks, pants, jumpsuits, skirt, left-leg, right-leg, left-shoe, right-shoe), and train the modified U_Net network on LIP dataset with grouped labels at first. We randomly chose 300 images of people from the trainset of Market-1501 [8], CUHK03 [17], and DukeMTMC-reID [9], and then labelled them with 4 semantic labels. Finally, we fine-tuned the modified U_Net network model on LIP dataset with labeled data. We use the fine-tuned model for part segmentation in the proposed person re-identification method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**NetModule** & **Branch01** & **Branch02** & **Branch03** & **Branch04** \\ \hline input & \(3\times 224\times 112\) & \(3\times 56\times 56\) & \(3\times 112\times 56\) & \(3\times 112\times 56\) \\ conv1 & \(64\times 112\times 56\) & \(64\times 28\times 28\) & \(64\times 56\times 28\) & \(64\times 56\times 28\) \\ res2 & \(64\times 56\times 28\) & \(64\times 14\times 14\) & \(64\times 28\times 14\) & \(64\times 28\times 14\) \\ res3 & \(128\times 28\times 14\) & \(128\times 7\times 7\) & \(128\times 14\times 7\) & \(128\times 14\times 7\) \\ res4 & \(256\times 14\times 7\) & \(256\times 4\times 4\) & \(256\times 7\times 4\) & \(256\times 7\times 4\) \\ res5 & \(512\times 7\times 4\) & - & - & - \\ pool5 & \(512\times 1\times 1\) & \(256\times 1\times 1\) & \(256\times 1\times 1\) & \(256\times 1\times 1\) \\ scaled\_pool5 & - & \(256\times 1\times 1\) & \(256\times 1\times 1\) & \(256\times 1\times 1\) \\ \hline F\_concate & & \(1280\times 1\times 1\) & \\ \hline F\_fuse & & \(512\times 1\times 1\) & \\ \hline softmax & & \(751\times 1\times 1\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The architecture of our four-branch network and its feature map size (Market-1501). Figure 5 illustrates some examples of parts semantic segmentation map and corresponding bounding boxes of human parts. The top row images are person images, and the bottom images illustrate their part segmentation by super-pixels and bounding boxes of person parts are demonstrated with red rectangles. It illustrates the results of parts localization in different situations, including normal situation (1 st column), leg occlusion (2 nd and 3 rd columns), head occlusion (4 th and 5 th columns), detection mistakes (6 th to 9 th columns) and crowds (10 th and 11 th columns). As seen from the localization results in different situations, semantic segmentation-based part localization is stable and accurate. There are also some mistakes. As seen from the 6 th column and the 10 th column, there are some segmentation mistakes in the torso part, which result in the width of the bounding box of torso part reduced by 7.14% in the 6 th column, and height of bounding box of lower-body part increased by 8.26% in the 10 th column.