## 1 Introduction  
Human parsing, a special semantic segmentation task, aims to segment the human body into multiple semantic parts at the pixel level. It plays a potential role in many vision applications, such as video surveillance [1], autonomous driving [2], person re-identification [3], human-computer interaction [4], and fashion synthesis [5]. Recent studies developed several solutions from different perspectives to boost the performance of this task. Previous work with CE2P [6] proved that context information and high-resolution maintenance are two key factors in human parsing solutions. Existing human parsing methods [7; 8; 9; 10] fuse multi-scale features to obtain context information. For instance, PSPNet [11] uses the Pyramid Pooling Module (PPM) to capture context information by applying average pooling operations. Although they enlarge the receptive field to aggregate multi-scale features, they fail to learn richer global context dependencies from a global perspective. In addition, many methods maintain high-resolution by directly manipulating the convolution operation to recover missing local information such as linear interpolation and transposed convolution. [PERSON] et al. [12] incorporated details of low-level feature maps. Previous work on CE2P took advantage of the PPM to obtain context information and utilized a high-resolution module to recover details. In our work, we observe that richer context information and enhanced detail information can be further fully captured. As shown in Figure 1, several unreliable prediction examples generated by CE2P reflect some drawbacks. When the labeled annotations in the human parsing dataset are imbalanced, pixels that do not belong to the human body are prone to misjudgment. This situation is shown in Figure 1a. Roughly more than half of the pixels are labeled as background, resulting in inaccurate predictions of body parts and clothing. Besides, it is difficult to distinguish symmetrical human body parts and a visual similarity of appearance. The left and right feet, left and right legs have similar appearances as well as similar positions but belong to different categories. For example, as shown in Figure 1b, the coat and upperclothes have similar appearances that can easily confuse labels with different semantics. Meanwhile, human pose variations and object occlusion may bring out wrong results, as is illustrated in Figure 1c, where the pixels belonging to the human body are missed. A simple \(1\times 1\) convolution is used to obtain low-dimensional features in CE2P, which is insufficient to learn low-level feature maps. This operation may cause failures in segmenting some small targets. Most of them are on the face or the neck, not on the legs; our goal is to enhance the detailed features. To obtain more accurate parsing results, we propose an enhanced context learning with Transformer for human parsing, which can improve the accuracy in the current field of human parsing both locally and globally. Different from the traditional CNN method, we introduce a Global Transformer Module (GTM) to leverage self-attention to  
Figure 1: Illustration of unreliable human parsing results in CE2P [6]. (**a**) Inaccurate human body area recognition; (**b**) Similar appearances are confused; (**c**) Unidentified human information. capture the global context. To compensate for the loss of detailed features, our method employs a Detailed Feature Enhancement (DFE) architecture to exploit low-level spatial information from CNN features. Specifically, the DFE model includes a channel attention module and a spatial attention module. Channel attention selectively focuses on a featured map, and the combination of the two modules can capture more comprehensive detailed information. We design this module without down-sampling to retain sufficient local structures for small targets. Therefore, small targets such as the glove, scarf and hat can be clearly identified.