1 INTRODUCTION  
1.1 Organization of the Survey  

2 CONCEPTS AND PRELIMINARIES  
2.1 What Is Few-Shot Learning?  
2.2 What Is Machine Learning And How It Is Different From Few-shot Learning?  
2.3 What Is Transfer Learning and How It Is Different From Few-shot Learning?  
2.4 What Is Meta-Learning and How It Is Different From Few-shot Learning?  
2.5 Datasets  
2.6 Taxonomy  

3 DATA AUGMENTATION TO EVALUATE THE TRUE DATA DISTRIBUTION WITH MAXIMUM PROBABILITY  

4 TRANSFER LEARNING BUILDS DATA-TO-LABEL MAPPINGS FOR SPECIFIC PROBLEMS  
4.1 Pre-training and Fine-Tuning  
4.2 Cross-Domain Few-shot Learning  

5 META-LEARNING DERIVES TASK-TO-TARGET MODEL MAPPINGS INDEPENDENT OF SPECIFIC PROBLEMS  
5.1 Learning Model Parameters  
5.2 Learning Metric Algorithm  
5.3 Learning To Transmit Information  

6 MULTIMODAL COMPLEMENTARY LEARNING OF SMALL SAMPLES WITH LIMITED INFORMATION  
6.1 Multimodal embedding  
6.2 Generate semantic information from images  

7 FSL APPLICATIONS IN COMPUTER VISION  
7.1 Few-shot Image Classification  
7.2 Few-shot Object Detection  
7.3 Few-shot Semantic Segmentation  
7.4 Few-Shot Instance Segmentation  

8 FUTURE DIRECTION AND OPPORTUNITIES OF FSL  
8.1 Better evaluation of data distribution  
8.2 Improving the robustness of data-to-label mapping  
8.3 Learn meta-knowledge more effectively from historical tasks  
8.4 Full convergence of multimodal information  

9 CONCLUSION