  Accurate classification of fine-grained images remains a challenge in
backbones based on convolutional operations or self-attention mechanisms. This
study proposes novel dual-current neural networks (DCNN), which combine the
advantages of convolutional operations and self-attention mechanisms to improve
the accuracy of fine-grained image classification. The main novel design
features for constructing a weakly supervised learning backbone model DCNN
include (a) extracting heterogeneous data, (b) keeping the feature map
resolution unchanged, (c) expanding the receptive field, and (d) fusing global
representations and local features. Experimental results demonstrated that
using DCNN as the backbone network for classifying certain fine-grained
benchmark datasets achieved performance advantage improvements of 13.5--19.5%
and 2.2--12.9%, respectively, compared to other advanced convolution or
attention-based fine-grained backbones.
