Multi-level parallel graph neural network based few-shot image classification algorithm
FSL leads to the reduction of feature diversity due to the small sample size.To compensate for this,it is proposed to obtain a more sufficient number of features by improving the feature extraction ability of the model.Multiple parallel graph neural networks are utilized for multiple feature extraction,so that the model can extract image features more fully and improve the classification accuracy of few-shot image classification tasks.The proposed multiple feature extraction method improves the classification accuracy of the baseline by 2.02%in 5-way 1-shot setting and by 1.98%in 5-way 5-shot setting.
NETL
NSTL
万方数据
维普
