# 2D Semantic Segmentation: Recent Developments and Future Directions
## 4 Key Technologies to Improve the Effect of Semantic Segmentation
### Development Trend of Encoder-Decoder Systems  
The advent of encoder-decoder architecture has notably influenced semantic segmentation over the past few years. The convolutional neural network model it employs has the capability to extract image feature data and transform it into semantic segmentation outcomes through the decoder. This effectively addresses the challenge of multiclass pixel categorization in semantic segmentation tasks, thereby enhancing both the precision and speed of segmentation. The encoder-decoder structure is composed of two segments(as shown in Figure 6). The encoder's role is to construct an efficient feature extraction network. As the network deepens, the feature map dimension progressively shrinks while the semantic features consistently grow. The decoder's aim, on the other hand, is to develop a feature map restoration model. Throughout the decoding stage, semantic information is articulated and details are continually enriched. Figure 5: PSPNet network structure [46]. As for FCN, it conserves spatial information by substituting the final fully connected layer of VGG16 with a convolutional one, maintaining the feature map in a two-dimensional arrangement. Further, this approach also remedies the issue of a required fixed input size. [PERSON] et al. [47] proposed the novel encoder-decoder network CENet to collaboratively explore hierarchical convolutional features for accurate pixel-level semantic segmentation. Primarily, two methodologies are used to strike a balance between the employment of superficial and deep features: encoding and decoding methods and multipath fusion techniques. The former concentrates on refining the structures of both encoding and decoding, hence enhancing the capability for information extraction and restoration. The latter places emphasis on the amalgamation of both types of information. Another hot direction for encoder-decoder architectures is the emergence of network architectures based on transformers, such as the Visual Transformer (Vit) [48] and its variants [49; 50; 51; 52]. Due to their powerful long-range dependency modeling capabilities, which have achieved breakthroughs in various vision tasks, their appearance provides a promising research direction for semantic segmentation [53]. Looking ahead, there remains immense potential for the evolution of semantic segmentation technology grounded in the encoder-decoder architecture. To expedite the progression of this technology, we postulate the following concepts are viable:  
1. Adaptive Feature Fusion: Traditional encoder-decoder architectures often rely on fixed feature fusion strategies. However, these strategies may not fully exploit the multiscale information in images. Therefore, we suggest continuing research on adaptive feature fusion methods that dynamically adjust the feature fusion weights based on the input image characteristics, thereby achieving more precise semantic segmentation. 2. Task-Oriented Encoder Design: Current encoder-decoder architectures often employ generic encoder structures such as ResNet and VGG. Although these generic encoders exhibit excellent feature extraction capabilities, they may not be optimized for specific semantic segmentation tasks. To address this issue, we propose designing a task-oriented encoder structure that incorporates modules specifically designed for semantic segmentation, thereby improving feature extraction effectiveness. 3. Dynamic Decoder Optimization: Traditional decoder structures usually adopt fixed upsampling and fusion strategies. However, these strategies may not fully recover the image's detail information. To address this issue, we suggest further investigation of dynamic decoder optimization methods by introducing adaptive upsampling and fusion strategies to dynamically recover detail information based on input image characteristics. 4. Integration of Vision Transformers (ViTs): Vision Transformers (ViTs) have achieved significant success in the computer vision field.