# 2D Semantic Segmentation: Recent Developments and Future Directions
## 4 Key Technologies to Improve the Effect of Semantic Segmentation
### Domain Adaptation  
Domain adaptation is a critical challenge that involves applying models trained in the source domain to the target domain, and it is a subset of transfer learning. Its goal is to leverage the knowledge learned in a different but consistent source domain to improve the model's effectiveness in the target domain [64]. Domain adaptation semantic segmentation is similar to semi-supervised semantic segmentation; the only difference between the two is whether a domain gap exists between labeled and unlabeled images. Domain adaptation methods can be divided into three main categories. 1. Instance-based domain adaptation: This method improves the classifier's performance in the target domain by weighting the samples from the source and target domains. Typical methods include maximum mean discrepancy and kernel mean matching. 2. Feature-based domain adaptation: This method improves the model's performance in the target domain by finding a mapping in the feature space that minimizes the distribution difference between the source and target domains. Typical methods include domain-invariant feature extraction and deep adversarial domain adaptation networks. 3. Adversarial domain adaptation: This method improves the model's generalization ability in the target domain by making the feature distributions similar in the source and target domains through adversarial training. Typical methods include generative adversarial networks (GANs) and domain adversarial neural networks. Domain adaptation in semantic segmentation is a challenging problem for two reasons. One of the reasons is that annotating labels is an expensive endeavor. Another reason is that the domain gap between the source and target domains limits the performance of semantic segmentation [65]. Previous domain-alignment strategies aim to explore the largest domain-invariant space to expand the knowledge learned in the target domain. Given the lack of guidance of the corresponding classes of the source domain, outlier classes and negative shifts arise. To address this issue, the authors of [66] proposed a partial domain adaptation method for semantic segmentation to guide the target model as it selectively learned category-level knowledge. A module called a partial adaptive map was introduced to incentivize the target model; abundant knowledge was acquired from non-outliers, and minimal knowledge was obtained from outliers, thus avoiding negative transfer. However, this approach has some limitations and challenges. Domain adaptation techniques require adequate consideration of the differences between the source and target domains, including data and feature distributions. Additionally, domain adaptation methods have technical restrictions such as the need for sufficient labeled data, appropriate feature selection and transformation methods, and careful consideration of different task characteristics. Domain adaptation, as an essential method in transfer learning, is important in various practical applications, particularly semantic segmentation. To overcome the challenges posed by existing technology (such as negative transfer, insufficient annotations, and multisource domain adaptation), future researchers should focus on the following aspects:  
1. Domain-specific information transfer methods: Domain-specific information transfer methods should be investigated by analyzing structured information, local and global semantic relationships, and high-order features within the domain. This task can help enhance the model's adaptability and generalization capabilities for domain differences. 2. Application of adversarial training in domain adaptation: Adversarial training strategies must be utilized to strengthen the model's robustness against the distribution differences between the source and target domains. By introducing domain-adversarial loss functions, the distribution gap between source and target domain features can be reduced, thus improving domain adaptation performance. 3.