# Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification
## 2 Related Work  
In this section, we present a brief review of works in feature reaction and part alignment for person re-identification. At the beginning of the study, hand-crafted features extractors, such as color histogram [13], Scale-Invariant Feature Transform (SIFT) [14], Local Binary Patterns (LBP) features [15], Bag of Word (BoW) [8] and Local Maximal Occurrence (LOMO) [16] are employed for the person representations. Recently, the methods based on deep learning learn feature representation directly from tasks and have shown significant improvement compared with hand-crafted feature extractors. All kinds of popular CNN network architectures, such as Inception network [3; 4; 5], Resnet network [6; 7], are applied to learn feature representation for person re-identification. Additionally, different loss functions, such as Softmax loss [17], Siamese loss [4; 18], Cluster loss [19], Triplet loss [20] and their combination [21] are  
Figure 1: Examples of part misalignment caused by pose variation, background clutter, detection errors, camera point of view variation, different accessories and occlusion. Images in top row are from Market1501 dataset [8], and images in bottom row from DukeMTMC-reID dataset [9]. used to improve the discriminative feature learning in person re-identification tasks. Softmax loss [17] function is the common loss function used in recognition tasks. Many scholars focus on person re-identification based on part alignment [7; 10; 17; 22; 23]. Early works divide the person image into many stripes or grids to reduce the effects of part misalignment. Article [10] divides the person image into three horizontal stripes and extracts CNN features of each strip. After that, they concatenate and fuse them with a fully connected layer to represent a person image. Meanwhile, DeepReID method [17] also divides the person image into horizontal stripes and carries out patches matching within each stripe. On the other hand, SpindleNet [22] takes the human body structure information into person re-identification pipeline to help align body part features of images. The features of different semantic levels are merged by a tree-structured fusion network based on human body region which is guided by multi-stage feature decomposition and tree-structured competitive feature fusion, to represent a person image. IDLA method [23] captures local relationships between the two input images on the basis of mid-level features of each input image, and computes a high-level summary of the outputs of this layer by a layer of patch summary features, which are then spatially integrated with subsequent layers. More stripes- and grids-based methods can be found in [7]. Although stripes- and grids-based methods reduce the risk of part misalignments, the division of grids or strips is predefined and heuristic, which can't locate parts precisely. Pose-based person re-identification methods leverage external cues from human pose estimation. Article [11] incorporates a simple cue of the person's coarse pose (i.e., the captured view with respect to the camera) and the fine body pose (i.e., joint locations) to learn a discriminative representation of person image. PDC method [5] leverages the human part cues to alleviate the pose variations and learn feature representations from both the global image and different local parts. To match the features from global human body and local body parts, a pose driven feature weighting sub-network is further designed to learn adaptive feature fusions. Pose-based methods leverage human pose estimation to infer the location of body parts. However, parts missing is ineluctable, it makes the convolutional neural network not work properly. And it is hard to find the right body part in the crowd because there may be several parts of the same semantic label in an image. Attention mechanism has a large impact on neural computation, which selects the most pertinent pieces of information and focuses on specific parts of their visual inputs to compute the adequate responses [24; 25; 26; 27; 28; 29].