Mamba-wavelet-based multi-scale modeling method for few-shot fine-grained image classification

Fine-grained few-shot image classification aims to recognize subtle inter-class differences under limited annotated 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, resulting 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 dependencies 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) module. 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 classification accuracy was achieved and stable performance improvements were obtained. It is concluded that the proposed network 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.