# 2D Semantic Segmentation: Recent Developments and Future Directions
## 4 Key Technologies to Improve the Effect of Semantic Segmentation
### Knowledge Distillation  
The concept of knowledge distillation was first introduced by [PERSON] in the paper titled \"Distilling the Knowledge in a Neural Network [59]\". This method involved the use of a Teacher Network (typically more complex yet with superior prediction accuracy) to train a Student Network characterized by a leaner structure, reduced complexity, and being more suited for inference deployment. The crux of the technique lies in utilizing a soft target related to the Teacher Network as part of the loss function, thereby facilitating the transfer of knowledge from the teacher to the student. In the field of semantic segmentation, knowledge distillation(as shown in Figure 9) can assist us in reducing the size and computational complexity of models without sacrificing accuracy. Semantic segmentation tasks typically require predictions at the pixel level, demanding that the model handle a vast amount of detailed information, thereby leading to significant model size and computational burden. Knowledge distillation, through enabling a smaller model to mimic the behavior of a larger one, can aid us in designing smaller, faster, and more efficient semantic segmentation models. Knowledge distillation can be broadly divided into three categories: Pixel-wise distillation, Pair-wise distillation, and Holistic distillation [60]. While these methods share the common goal of transferring knowledge, each offers a distinct approach to distillation. Pixel-wise distillation focuses on individual pixel values, Pair-wise distillation compares the relationship between pairs of instances, and Holistic distillation aims at capturing more global features. Thus, these distillation methods provide diverse means of enhancing the learning capability of the Student Network. Figure 8: Schematic diagram of dilated convolutions. [PERSON] et al. [61] proposed a prediction model based on an encoder-decoder architecture (called SPNet) to further determine additional unlabeled data; they presented a knowledge distillation scheme to distill the structured knowledge from cumbersome to compact networks. Without using any pretrained models, this method demonstrated its state-of-the-art performance on several challenging data sets. On the cityscape test data set, it achieved 75.8% mIoU at 61.2 FPS. [PERSON] et al. [62] proposed a multieacher framework. Experiments validated that the multieacher framework resulted in an improvement of 9% to 32.18% compared to the single-teacher paradigm. Moreover, it was demonstrated that paradigm surpassed previous supervised real-time studies in the semantic segmentation challenge. [PERSON] et al. [63] proposed a simple yet general and effective knowledge distillation framework called double similarity distillation (DSD) to improve the classification accuracy of all existing compact networks by capturing the similarity knowledge in pixel and category dimensions. Extensive experiments on four challenging data sets demonstrated its effectiveness and generality. However, knowledge distillation requires some computing resources and time to train the large model and transfer knowledge to the small model, resulting in additional computational costs. Moreover, during the knowledge distillation process, issues such as incomplete knowledge transfer and information loss may arise, thereby affecting the accuracy and performance of the model. Based on existing research, we believe that the following directions hold significant potential for further investigation:  
1. Adaptive Knowledge Distillation: Traditional knowledge distillation methods often require manually setting fixed loss weights. To reduce human intervention and improve distillation performance, it is worth exploring adaptive methods for adjusting loss weights. This can be achieved by introducing dynamic weight allocation strategies that automatically adjust loss weights based on the model's performance during training. 2.