Research on Cross-domain Image Spatial Data Few-shot Learning
Cross-domain image spatial data Few-Shot Learning(FSL)aims to train a reliable model using a small amount of labeled image spatial source domain data to classify image target domain data with large distribution differences.It is a hot topic in the field of machine learning research in recent years.An overview of the main current cross domain image spatial data FSL models is provided.Based on the main ideas of model problem-solving,the models are classified into data introduction method,feature enhancement method,parameter control method,and hybrid method.Among them,the data introduction is subdivided into single source domain data,multi-source domain data,and target domain data.The feature enhancement method is subdivided into feature transformation and feature fusion.The hybrid method is subdivided into the combination of different methods and the combination of different types of loss functions,the principles,advantages,and disadvantages of different methods are summarized.At the same time,a detailed introduction to the commonly used datasets and benchmarks for cross domain image space FSL is also provided,and the experimental results of classical models on mainstream benchmarks are compared and analyzed.Finally,the challenges of the cross-domain image spatial data learning with FSL is summarized,and the future research directions is pointed out.
万方数据
维普
