# EHANet: An Effective Hierarchical Aggregation Network for Face Parsing
## 2 Related Work  
**Face Parsing. ** Early works mainly focused on exemplars and graphical models. [PERSON] et al. [12] combined the conditional random field and the Boltzmann machine to model both local and global structure in face segmentation. [PERSON] et al. [13] integrated the CNN into graphical models for structured prediction problems. [PERSON] et al. [11] proposed an exemplar-based segmentation algorithm that exploits landmarks and SIFT features to transfer partial masks from aligned exemplars to the test images. With the popularity of DCNNs, a lot of CNN-based work has emerged. [PERSON] et al. [14] proposed a pixel-level face parsing network by combining shallow CNN and spatial variant RNN. [PERSON] et al. [15] designed an encoder-decoder network for face parsing with the help of a novel adaptive prior mechanism. Considering that a traditional crop-and-resize pipeline may ignore the contextual area outside the regions of interest (such as hair), [PERSON] et al. [16] proposed a \"RoI tanh-warping\" image processing method, which achieved a state-of-the-art result. **Lightweight Networks. ** [PERSON] et al. [17] first proposed a lightweight model called SqueezeNet, which employs several 1 \(\times\) 1 convolutions and parallel convolutions. While the performance is equivalent to AlexNet, the calculation amount of the model parameters is reduced by eight times. MobileNet [18, 19] introduces deep separable convolutions to achieve low-latency results without a significant drop in accuracy. From the perspective of optimizing the network structure, ShuffleNet [20, 21] fuses the operations of group convolution and channel shuffling to ensure the information flow and dimensionality reduction between channels. ShuffleNet v2 innovatively establishes four guidelines, which are very helpful for the design of lightweight architectures. Octave convolution [22] reduces the size of low-frequency features through feature sharing in adjacent locations, thereby reducing feature redundancy and memory consumption. Some recent solutions [23, 24, 25] mainly refer to the above modules and channel pruning [26] tricks to reduce model runtime. **Contextual Information. ** A great deal of research has focused on exploiting contextual information to enhance the representation capabilities of segmentation. Global pooling is widely used in various backbones to obtain the contextual information for global representation. Dilated convolution [27] expands the receptive field by introducing an expansion rate, which is commonly used in semantic segmentation tasks. DFANet [23] aggregates discriminative features through sub-network and sub-stage cascade, respectively. PSPNet [28] uses multi-scale pyramid pooling to obtain features at different scales. ACFNet [29] harvests the contextual information from a categorical perspective. Recently, ExFuse [30] has been proposed to improve the low-level context through additional supervision of the encoder. **Attention Mechanism. ** Computer vision draws on the attention mechanism of natural language processing, and produces many wonderful results. SeNet [31] relies on context to automatically obtain the importance of each feature channel through self-learning. CBAM [32] combines spatial and channel attention mechanisms. Compared to SeNet, which only focuses on channels, CBAM can achieve better results. Based on CBAM's dual-path attention, DANet [33] directly uses non-local autocorrelation matrices for operations, avoiding tedious operations. CCNet [34] proposes a novel vertical and horizontal attention module that can be used to capture contextual information from remote dependencies in a more efficient way. **Boundary Supervision. ** Many studies have confirmed that boundary supervision can further sharpen and refine the edge contour prediction. CE2P [35] improves edge segmentation in a multi-task learning manner by introducing additional boundary supervision in human parsing task.