Abstract: Few-shot image classification addresses the problem of poor performance in traditional image classification when data is scarce.The challenge lies in effectively utilizing sparse sample label data to predict the true feature distribution.To tackle this,some recent methods adopt data augmentation techniques such as random mas-king or mixed interpolation to enhance the diversity and generalization of data label samples.However,there are still the following issues:1)Due to the uncertainty of random masking,situations where the foreground is either completely masked or exposed may occur,leading to the loss of crucial information in samples;2)Because the data distribution after mixed interpolation tends to be overly uniform,models find it difficult to accurately distinguish differences between different classes,thus failing to effectively delineate boundaries between different categories.To address these problems,this paper proposes a data augmentation method based on Saliency Mask Mixup.Firstly,through Mask Mix(M-Mix) and Confident Clip Selector(CCS),adaptive selection and retention of key feature information in images are performed.Secondly,using Saliency Fuse(SF),the importance of various regions in the image is calculated to guide image fusion,making the resulting images more diverse and rich,thereby making category boundaries clearer.The proposed method demonstrates outstanding performance on multiple standard few-shot image classification datasets(such as miniImage-Net,tiered-ImageNet,Few-shot CIFAR100,and Caltech-UCSD Birds-200),outperforming state-of-the-art methods by approximately 0.2~1%.These results indicate significant potential and advantages of the proposed method in few-shot image classification.

Key words: Few-shot learning, Image classification, Contrastive learning, Date mixing, Data augmentation, Saliency map