FwNet-ECA: A Lightweight Fourier-Attention Network for Fine-Grained Image Classification

Window-based attention mechanisms have been introduced to alleviate the excessive computational cost inherent in global attention mechanisms. In this paper, we propose a novel architecture named FwNet-ECA, which integrates the Fourier transform with learnable weight matrices to enhance spectral features of images. By performing filter enhancement after window-based attention, our method establishes a global receptive field, thereby overcoming the limited receptive field typically associated with windowed attention. Furthermore, we incorporate the existing Efficient Channel Attention module to improve interchannel information exchange. Unlike approaches that rely on physical window shifting, our method leverages frequency-domain enhancement to implicitly connect spatial regions. We evaluate our model on the iCartoonFace dataset and demonstrate competitive performance on fine-grained classification benchmarks. Experimental results show that, compared to shift-based window methods, our model achieves comparable accuracy with fewer parameters and lower computational overhead. Moreover, visualization analyses clearly indicate that the filter enhancement technique is particularly effective in the shallow layers of the network, where feature maps are relatively large. This work presents an effective solution to the limited receptive field problem in window-based attention mechanisms. The code is publicly available at https://github.com/qingxiaoli/FwNet-ECA .