小样本细粒度图像分类的Mamba-小波多尺度建模方法OA

Fine-grained few-shot image classification aims to recognize subtle inter-class differences under limited anno-tated samples and has been widely applied in intelligent recognition,ecological monitoring,and autonomous driving.However,existing convolutional architectures are constrained by fixed receptive fields and local modeling schemes,re-sulting in insufficient characterization of multi-scale feature relationships.Although attention-based or frequency-domain methods have improved the discriminability of fine-grained features,limitations still exist in modeling cross-scale depen-dencies and feature fusion.To address these issues,a Mamba-wavelet-based multi-scale modeling method for few-shot fine-grained image classification was proposed.Specifically,a multi-scale feature relation network(MSFRNet)based on Mamba state space modeling was constructed.The proposed network consisted of two core modules,namely a wavelet-guided dynamic Mamba multi-scale feature extraction(WDMFE)module and a cross-scale attention fusion(CAF)mod-ule.In the WDMFE module,a wavelet-guided dynamic adaptive Mamba structure was introduced to enhance frequency perception and contextual modeling across different scales.In the CAF module,multi-scale features were integrated through channel and spatial attention mechanisms to achieve cross-scale feature complementation.Experimental results on benchmark datasets,including CUB-200-2011,Stanford Dogs,and Stanford Cars,demonstrated that higher classifica-tion accuracy was achieved and stable performance improvements were obtained.It is concluded that the proposed net-work effectively enhances fine-grained feature representation and cross-task generalization ability,and provides a scalable framework for multi-scale modeling in few-shot fine-grained classification.