Few-shot Image Classification Algorithm Base on D-ResNeXt Backbone Network
Few-shot image classification is currently one of the most important directions in the field of artificial intelligence.In this area,the method based on metric learning is concise and efficient.To address the problem of the backbone network used in the fea-ture extraction stage of current image classification,most existing works use traditional residual networks,which extracts poorly the features of images with large intra-class differences as the method is influenced by the dataset.ResNeXt is an upgraded version of the traditional residual network ResNet,optimizing the problem of low accuracy and large errors in the feature extraction stage of the traditional network.According to its network characteristics,this paper designs a network variant suitable for small sample mod-els,which uses its variant as a backbone network to improve its feature extraction ability,and combines two attention modules to fur-ther improve the recognition effect of intra-class similarity and inter-class variability of images,reduce the influence of irrelevant factors,and effectively improve the overall classification accuracy.
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