# Enhanced Atrous Extractor and Self-Dynamic Gate Network for Superpixel Segmentation
## 2 Related Work  
**Superpixel segmentation**: Superpixel segmentation aims to group pixels with similar low- and mid-level properties. We consider the group of pixels as a superpixel, which can reduce the computation cost. Traditional superpixel segmentation algorithms mainly introduce k-means-based methods, which compute the associate map between the seed and surrounding pixels. SLIC [15] initializes the seeds and computes the associate map of the pixels and the superpixel. Then, it assigns every pixel a label based on the associate map. Finally, it computes the average of the pixels labeled to define a new seed. LSC [17] first maps the RGB image to the 10-dimension feature space and computes the associate map. SNIC [16] first initializes centroids and uses a priority queue to assign the pixels to the correct centroid. The manifold SLIC [15] introduces a two-dimension manifold to compute a content-sensitive superpixel map. However, these k-means-based methods are non-differentiable and can not be incorporated into the convolutional neural network. Therefore, to fix the problem, some deep-learning methods are proposed. SSN [18] proposes a differentiable soft associate map and introduces a convolutional neural network to extract features. The SCN [19] first proposes an end-to-end Unet architecture to predict the superpixel map. However, these methods often result in the under-segmentation and low compactness of the remote sensing image. To fix the problem, we propose EAGNet, an enhanced atrous extractor and self-dynamic gate network. The enhanced atrous extractor can extract the multi-scale superpixel feature, and the self-dynamic gate can fuse the feature dynamic, which can fix the low compactness and under-segmentation, respectively. **Vision transformer**: Superpixel segmentation aims to group pixels with similar low- and mid-level properties. We consider the group of pixels as a superpixel, which can reduce the computation cost. A traditional transformer is an effective technology that was first proposed by the natural language process field. The development of the transformer raised the attention of the computer vision field rapidly. Vit is the first vision transformer model in the computer vision field. Vit [22] introduces the convolution to divide the \(16\times 16\) dimension of the patch into an embedding space and compute the self-attention of these patches. Swin transformer [23] proposes the swift window self-attention and hierarchy transformer to learn the powerful feature representation. PVT [24] first combines convolution and self-attention to reduce the dimension of the feature and provide the multi-scale feature for the downstream task. However, all these methods still suffer from the huge computation cost. In order to reduce the computation cost, some lightweight methods are proposed to reduce the computation cost. An example of this is MiniVit [25], which introduces the distillation and teacher-student model to achieve weight multiplexing, which reduces the computation cost largely. The Davit [26] introduces spatial-wise self-attention and channel-wise self-attention to reduce the computation cost. EfficientNet [27] combines the CNN block and transformer block to reduce the computed number of self-attention. However, all these methods still suffer from high latency, and it is hard to extract the multi-scale feature efficiently. To fix these problems, we propose the enhanced atrous extractor, which is a transformer-based architecture but a pure CNN feature extractor. Different from previous methods, we introduce the non-local atrous convolution to replace the self-attention to extract the multi-scale superpixel feature with context information. The latency of our proposed enhanced atrous extractor can satisfy the requirement of superpixel segmentation. **Gating mechanism**: The gating mechanism is a technology that can control the passing of information. It was first proposed by LSTM [28], which is the basic block of RNN [29].