# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 6 Future Research Trends  
In this Section, we present some promising techniques towards improving the performance of semantic image segmentation that can be adopted in future research efforts.  
* Transfer learning: Transfer learning transfers the knowledge (i.e., weights) from the source domain to the target domain, leading to a great positive effect on many domains that are difficult to improve because of insufficient training data [66]. By the same token, transfer learning can be useful in real-time semantic segmentation by reducing the amount of the needed training data, therefore the time required. Moreover, as [47] proposes, transfer learning offers a greater regularization to the parameters of a pre-trained model. In [67; 68], the use of transfer learning improved the semantic segmentation performance in terms of accuracy.  
Figure 27: Performance in terms of FPS in relation to mIoU for the state-of-the-art models in (**a**) “Cityscapes” dataset and (**b**) “CamVid” dataset.  
Domain adaptation: Domain adaptation is a subset of transfer learning. Domain adaptation's goal is to ameliorate the model's effectiveness on a target domain using the knowledge learned in a different, yet coherent source domain [69]. In [70] the use of domain adaptation, achieved a satisfactory increase in mIoU on unseen data, without the adding extra computational burden, which is one of the great goals of real-time semantic segmentation. Thus, domain adaptation might be a valuable solution for the future of autonomous driving, by giving accurate results on unseen domains while functioning in low latency.
* Self-supervised learning: Human-crafting large-scale data has a high cost, is time-consuming and sometimes is an almost impracticable process. Especially in the field of autonomous driving, where millions of data are required due to the complexity of the street scenes, many hurdles arise in the annotation of the data. Self-supervised learning is a subcategory of unsupervised learning introduced to learn representations from extensive datasets without providing manually labeled data. Thus, any human actions (and involvements) are avoided, reducing the operational costs [71].
* Weakly supervised learning: Weakly supervised learning is related to learning methods which are characterized by coarse-grained labels or inaccurate labels. As reported in [71], the cost of obtaining weak supervision labels is generally much cheaper than fine-grained labels for supervised methods. In [72], a superior performance compared to other methods has been achieved in terms of accuracy, for a benchmark that uses the \"Cityscapes\" and \"CamVid\" datasets. Additionally, ref. [73] with the use of classifier heatmaps and a two-stream network shows greater performance in comparison to the other state-of-the-art models that use additional supervision.
* Transformers: ref. [74] allows the modeling of a global context already at the first layer and throughout the network, contrary to the ordinary convolutional-based methods. Segmenter approach reaches a mean IoU of 50.77% on ADE20K [75], surpassing all previous state-of-the-art convolutional approaches by a gap of 4.6%. Thus, transformers appear to be promising methods for the future of semantic segmentation.