## 2 Related Work
### Attention Captures Pixel Contextual Information  
Attention is the main means to address the problem of information overload, which is widely used in various tasks. Squeeze-and-Excitation networks enhance representation by modeling channel relationships in the attention [31]. [PERSON] et al. used several attention masks to fuse feature maps or predictions of different branches [32]. [PERSON] et al. applied the self-attention model to machine translation [33].  
The attention model was first applied in the image recommendations to generate dense pixel-level contextual information; a point-wise spatial attention network (PSANet) realized long-distance contextual aggregation in scene analysis by learning point position-sensitive contextual dependency and two-way information propagation mode [34]. Non-local network utilizes self-attention to directly capture the long-distance dependence by calculating the interaction between any two locations, thus obtaining the complete image contextual information [33; 35].  
The complete image dependency provides useful contextual information to enhance image feature representation. Deeplabv2 proposes that atrous spatial pyramid pooling (ASPP) uses filters to detect the incoming convolutional feature layers at multiple sampling rates and effective field of view, to capture objects and image context at multiple scales [7]. Densely connected atrous spatial pyramid pooling (DenseASPP) densely connects a set of atrous convolutional layers to generate multi-scale features that cover larger sizes and densely cover that scale range [36]. Pyramid scene parsing network (PSPNet) uses a pyramid pool to mine global contextual information based on contextual aggregation in different regions [37]. Recently, [PERSON] et al. proposed a point-based spatial attention network, which aggregates remote contextual information by connecting all other locations with an adaptive predictive attention map [34]. Conditional random field (CRF) and Markov random field (MRF) are also used to capture the long-distance dependence of semantic segmentation [7; 38]. Object context for semantic segmentation (OCNet) and dual attention network (DANet) use non-local blocks to obtain contextual information [35; 39; 40]. [PERSON] et al. proposed the CCNet model to collect the contextual information of all pixels on the cross path and capture the complete image correlation through further loop operation [10]. However, this method is not comprehensive enough to extract and may ignore key information, resulting in low accuracy in extraction and high computational complexity.  
Existing studies have made a lot of contributions in capturing image contextual information by using attention and can aggregate remote contextual information. However, for each pixel point, capturing pixel contextual information is not comprehensive enough, which may ignore key information, resulting in low accuracy in extraction and insufficient integrity and reliability in the semantic feature description. If attention can be proposed to collect the complete pixel contextual information more accurately and comprehensively, it can not only enrich the item information but can also enhance the semantic feature description of the image and mine the users' interest more comprehensively.