# 2D Semantic Segmentation: Recent Developments and Future Directions
## 4 Key Technologies to Improve the Effect of Semantic Segmentation
### Method Based on Multimodal Fusion  
With the rapid development of deep learning, its excellent performance in various scene understanding tasks has been well demonstrated. However, in some complex or challenging conditions, multiple modalities need to be used in the same scene to provide complementary information. Multisource data fusion is a key step in technological development, and the rise of artificial intelligence technology has promoted the development of sensor fusion technology. The current research direction is to achieve semantic segmentation of multimodal data fusion by utilizing data acquired from multiple sensors. This  
Figure 11: Schematic diagram of attention mechanism, * representing the process of weighted summation. Figure 10: Few-shot semantic segmentation framework. Picture from “Few-Shot Semantic Segmentation with Prototype Learning”. method can effectively improve the performance and robustness of semantic segmentation algorithms [90; 91; 92]. Examples include fusing RGB and thermal depth images [93] and fusing RGB images and lidar point clouds [94]. [PERSON] et al. [95] developed a multimodal fusion network as a joint encoding model for verification. With this multimodal fusion assumption, multiple multimodal models were built in accordance with the proposed fusion method and evaluated on the KITTI and A2D2 data sets. The best fusion network achieved an over 85% lane line accuracy and an over 98.7% overall accuracy. The method based on multimodal fusion has advantages and disadvantages when applied to semantic segmentation. The advantages lie in the method's ability to incorporate complementary information from multiple modalities, which can improve the accuracy and robustness of the segmentation results. However, the fusion of different modalities may also introduce additional noise and uncertainty, which can negatively affect segmentation performance. In addition, the fusion process itself can be computationally intensive and may require specialized hardware or software for efficient implementation. To effectively address the aforementioned challenges, future researchers should focus on the following aspects:  
1. Introducing adaptive fusion strategies: Previous multimodal fusion methods mainly relied on fixed fusion strategies. However, in different scenarios and applications, the importance of information from different modalities may vary. Therefore, introducing adaptive fusion strategies that dynamically adjust the weights of different modalities in accordance with the scene context or application requirements can improve fusion results. This goal can be achieved using attention mechanisms, which allow the network to automatically determine the contributions of different modalities. 2. Utilizing GCNs: Given that GCNs can effectively process irregular structured data, they can be considered for multimodal fusion. By representing multimodal data as graph structures, GCNs can successfully capture the relationships between different modalities, further improving fusion performance. 3. Cross-modal self-supervised learning: A major challenge in multimodal fusion is effectively transferring information between different modalities. By introducing cross-modal self-supervised learning, models can automatically learn how to share information between modalities. This approach can be realized through alignment and generation tasks such as reconstructing one modality's data by generating another modality's data. 4. Adopting multiscale fusion strategies: Information from different modalities may be complementary on different scales. To fully exploit this feature, multiscale fusion strategies can be adopted. By fusing on different spatial scales, the local and global relationships between modalities can be captured, thus enhancing fusion performance. 5. Combining domain adaptation with multimodal fusion: To further improve the robustness of multimodal fusion, domain adaptation techniques can be combined with multimodal fusion.