Prototype Rectification Few-Shot Classification Model with Dual-Path Cooperation

Abstract

In the learning process of the metric-based meta-learning, there are some problems, such as the lack of prior knowledge acquired due to the distribution of scarce data, the interference of weakly related or unrelated features extracted from a single-view sample, and the deviations of representative features caused by classification. To solve these problems, a prototype rectification few-shot classification model with dual-path cooperation is proposed in this paper. Firstly, the dual-path cooperation module adaptively highlights key features and weakens weakly related features from a multi-view perspective, and makes full use of feature information to obtain prior knowledge to improve the expression ability of features. Secondly, the problem of intra-class prototype with deviations is solved by the prototype rectification classification strategy with the sample feature information of the query set. Finally, the model parameters are updated reversely by means of the loss function, and the classification accuracy of the model is improved. Comparative experiments of 5-way 1-shot and 5-way 5-shot are conducted on five public datasets. Compared with baseline model, on the miniImageNet dataset, the accuracy is increased by 5.57 percentage points and 3.90 percentage points. On the tieredImageNet dataset, the accuracy is increased by 5.68 percentage points and 3.93 percentage points. On the CUB dataset, the accuracy is increased by 6.93 percentage points and 3.13 percentage points. On the CIFAR-FS dataset, the accuracy is increased by 8.03 percentage points and 1.65 percentage points. On the FC-100 dataset, the accuracy is increased by 4.25 percentage points and 4.89 percentage points. Experimental results show that the proposed model has good performance in the field of few-shot learning, and the modules in the model can be migrated to other models