Research on few shot learning methods in image recognition

The current mainstream image recognition methods require large-scale data annotation, which limits the application of image recognition methods in practical tasks. Few shot Learning methods can solve this problem. This article starts with the research background and current status of few shot Learning , mainly introducing three mainstream learning methods in few shot learning , including meta learning, metric learning, and data augmentation. It elaborates on their design ideas and advantages and disadvantages, analyzes their performance on public datasets, and finally discusses the difficulties and challenges faced by few shot Learning . In the summary section, future research prospects are presented.