Few-Shot Image Classification Based on Multi-Scale Label Propagation

Under the condition of few-shot, due to the problem of low data, in other words, the labeled data is rare and difficult to gather, it is very difficult to train a good classifier by traditional deep learning. In recent researches, the method based on measuring low level local information and TPN(transductive propagation network) has achieved good classification results. Moreover, local information can measure the relation between features well, but the problem of low data still exists. In order to solve the issue of low data, MSLPN (multi-scale label propagation network) based on TPN is proposed in this paper. The core idea of the method is to use a multi-scale generator to generate image features of multiple scales, obtain the similarity scores of samples with different scale features through the relational measurement module, and obtain classification results by integrating similarity scores at different scales. Specifically, the method firstly generates multiple image features of different scales through a multi-scale generator. And then, the similarity scores of the multi-scale information are used for label propagation. Finally, classification results are obtained by calculating the multi-scale label propagation results. Compared with TPN, in miniImageNet, the classification accuracy of 5-way 1-shot and 5-way 5-shot settings is increased by 2.77% and 4.02% respectively. While in tieredImageNet, the classification accuracy of 5-way 1-shot and 5-way 5-shot settings is increased by 1.16% and 1.27% respectively. The experimental results show that the proposed method in this paper can effectively improve the classification accuracy by using multi-scale feature information.