# 2D Semantic Segmentation: Recent Developments and Future Directions
## 4 Key Technologies to Improve the Effect of Semantic Segmentation
### Attention Mechanism  
An attention mechanism [82] is a visual mechanism that mimics humans. Human vision can pay more attention to the target area while suppressing other temporarily useless information. An attention mechanism can be viewed as a dynamic weight adjustment process based on the features of the input image. Attention mechanisms in deep learning are similar to human visual attention mechanisms [83]. [PERSON] et al. [84] proposed a non-local operation to obtain image features. With the introduction of channel attention and spatial attention [85], different attention mechanisms have been applied to networks for semantic segmentation. Currently, attention mechanisms are mainly attached to the encoder-decoder structure.  
[PERSON] et al. [86] constructed a pyramid attention network for semantic segmentation that exploited global context information for semantic segmentation by bypassing the decoder network and combining an attention mechanism and spatial pyramid for pixel-level extraction of precise dense features. [PERSON] et al. [87] proposed a dual-attention network that could capture contextual dependencies based on a self-attention mechanism. [PERSON] et al. [88] added an attention mechanism to the model based on the DeepLab V3+ semantic segmentation model and designed an image semantic segmentation model based on the attention mechanism. The experimental results showed that the model had a higher segmentation accuracy and running efficiency than the DeepLab V3+ model, U-Net model, and SegNet model. The authors in [89] proposed a hybrid attention semantic segmentation network (HAssNet) based on the FCN model that could extract the object and its surrounding environment through the large receptive field of the multiscale object. A spatial attention mechanism was introduced into the FCN, and a channel attention mechanism was designed to achieve semantic consistency. Experimental results on open remote sensing data sets showed that the MIoU of HAssNet was improved by an average of 6.7% compared with that of state-of-the-art segmentation networks.  
The ultimate goal of attention mechanisms is to obtain abundant critical information on the current task among the many pieces of information; however, with the addition of an attention mechanism, the number of parameters of the model increases. Rational use of attention mechanisms can improve semantic segmentation accuracy. The emergence of numerous attention mechanisms can further promote the development of semantic segmentation.  
The use of attention mechanisms in semantic segmentation tasks has two main advantages(as shown in Figure 10). On the one hand, it can enhance the model's focus on the target objects, reduce the interference of background noise, and improve the accuracy of segmentation. On the other hand, attention mechanisms can improve the recognition and utilization of feature information at different scales, allowing the model to effectively adapt to objects of different scales.  
However, attention mechanisms also have some drawbacks such as the need for network structure improvement(as shown in Figure 11), which increases computational complexity and consumes storage space. Additionally, attention mechanisms have a strong dependence on training data, which may lead to overfitting.  
Future research directions include the establishment of efficient attention mechanisms that can improve segmentation accuracy without increasing computational complexity. Another direction is to study the applicability of attention mechanisms in different scenarios and investigate their effects on different data sets and tasks. Additionally, combining attention mechanisms with other techniques such as depth-separable convolution and graph convolutional networks (GCNs) can further enhance the performance of semantic segmentation.