# 2D Semantic Segmentation: Recent Developments and Future Directions
## 4 Key Technologies to Improve the Effect of Semantic Segmentation
### Few-Shot/Zero-Shot Semantic Segmentation  
Weakly supervised learning [67] and unsupervised learning [68] are becoming active research areas. Zero-shot and few-shot learning methods were introduced based on the premise that humans can recognize new concepts in a scene by using only a small amount of visual sample data. In the past, unsupervised segmentation was achieved by applying clustering algorithms such as K-means and graph cut [69] on hand-crafted image features. The few-shot semantic segmentation (FSS) method recognizes objects by leveraging few annotated examples, and it has a remarkable potential to segment new objects with samples annotated with few pixels. FSS places general semantic segmentation in few-shot scenarios in which the model performs dense pixel labeling of new classes with only a small number of support samples. However, existing FSS methods rely heavily on the integrity of visible image information. Therefore, a challenge in FSS is that under insufficient lighting or complex working conditions, visible images often fail to gain enough information, resulting in a sharp decrease in segmentation performance [70]. In this context, researchers have exploited the complementary and similar information of visible and thermal images to improve the performance of FSS [71]. The zero-shot semantic segmentation (ZSS) method uses an embedded vector to generate visual features under the condition of zero training samples [72; 73; 74; 75; 76]. Generally, pixels are aligned with semantic texts, and the segmentation model is applied to invisible object categories. This direction is promising because it can break the restriction in the number of segmentation categories. The main drawback of the ZSS method is that its prediction ability is insufficient, and it cannot distinguish the seen class from the unseen class. Notably, a broad ZSS (GZSL) was proposed in Ref. [77]. GZSL can identify seen and unseen classes simultaneously. The training of the feature extractor is achieved without considering semantic characteristics. The GZSL method decreases the prediction performance for the unseen class [78]. Recent work has been conducted on ZSS. To further reduce the burden of annotations, researchers introduced a challenging task called weakly supervised zero-shot semantic segmentation (WZSS). WZSS improved the performance of the HIOU of the model from 25.9 to 31.8. The results for HIOU (31.8) and MIO (22.0) in Ref. [79] were obtained on the PASCAL VOC 2012 data set. The researchers in [80] also proposed a novel context-aware feature generation network. This network can represent pixel-level context information based on category-level semantics and synthesize context-aware pixel visual features for the unseen classes. Experiments on PASCAL VOC, PASCAL Context, and COCO-Stuff showed that this method is much better than the existing zero-lens semantic division method because medical terms are specially set in the professional field and difficult to obtain. Therefore, the researchers in [81] put forward a new ZSL paradigm with three main contributions. First, prior knowledge of the division target was extracted from the previous model, which was called a relationship prototype. Second, a cross-mode adaptation module that could extend the prototype to the zero-shot model was developed. Lastly, a relationship prototype perception module was proposed to allow the zero-shot model to perceive the information contained in the prototype. Numerous experiments showed that the framework was much better than the existing framework. The advantage of few-shot and zero-shot semantic segmentation methods is their ability to perform semantic segmentation in cases of data scarcity or when no annotated data are available, which makes them highly practical and economical. Furthermore, these methods can support model transferability, enabling their application to new scenarios and tasks. However, few-shot and zero-shot semantic segmentation methods have certain limitations.