# A Multi-Path Semantic Segmentation Network Based on Convolutional Attention Guidance
## 1 Introduction  
Semantic segmentation is a long-standing topic in the computer vision domain that has attracted increasing attention in both academia and industry. Semantic segmentation models have undergone significant architectural revolutions, starting from the early convolutional neural network (CNN)-based models (e.g., FCN [1] and the DeepLab series [2; 3; 4]) to the more recently published Transformer-based methods (e.g., SETR [5] and SegFormer [6]). Compared with image classification, semantic segmentation is a dense and more precise prediction task that requires the handling of object boundaries and other details. CNN frameworks emphasize multi-scale information interaction, utilizing multi-scale contextual fusion and combining various dilated convolutions and pooling operations to aggregate multi-scale contexts. However, they cannot aggregate global information. Transformer-based models, on the other hand, address this problem effectively by splitting the input image into patches and linearly embedding them into sequences. However, they require higher computational complexity, especially when dealing with high-resolution images (e.g., remote sensing images). Moreover, Transformer-based models lack some of CNNs' inherent inductive biases, such as translation equivariance and locality, which can result in some details being ignored during global extraction.  
To overcome the above-mentioned problems, we propose a novel semantic segmentation network, which incorporates a multi-path encoder-decoder structure with convolutional attention. Our model is partially inspired by SegNext [7]. Building upon the visual attention network (VAN) [8], SegNext replaces the self-attention module with a multi-scale convolutional module. To reduce the computational overhead, a simple element-wise multiplication is applied to implement spatial attention, which integrates multi-scale features.  
For the decoder, features from different layers are extracted, and a Hamburger model [9] is implemented to extract the global context. In addition, a spatial and channel reconstruction module is incorporated in the main path of our model to enhance feature interaction, which also removes redundant features. Multi-path encoding allows the model to use convolutional attention to extract overall features without ignoring details and boundary information. The main contributions in this paper can be summarized as follows:  
1. A multi-path convolutional self-attention structure is proposed to enhance the learning of advanced semantic information. It also integrates global information and focuses more on the boundary information.
2. A spatial and channel reconstruction module is developed to reinforce feature interaction, which also eliminates redundant information.
3. Extensive experiments are conducted on mainstream datasets, where our model exhibits superior performances against other popular methods.  
The rest of the paper is organized as follows: related works are discussed in Section 2. The architecture of our model is described in detail in Section 3. The experimental results an ablation studies are presented in Section 4. A final conclusion is drawn in Section 5.