Spatial Gated Fusion Attention in ConvNeXt: A Novel Dual-Branch Architecture for Lightweight Image Classification

To enhance the feature extraction capability and computational efficiency of Convolutional Neural Networks (CNNs) in image classification tasks, this paper proposes a novel attention-augmented architecture, SGFA-ConvNeXt, based on the ConvNeXt backbone. The model embeds Spatial Gated Fusion Attention (SGFA) modules at critical transition points of each stage. These modules adopt a dual-branch parallel structure to model salient features along spatial and channel dimensions. The spatial branch combines multi-scale pooling with depthwise convolution to effectively capture long-range dependencies, while the channel branch utilizes global average pooling to recalibrate channel weights for feature refinement. Ultimately, the two branches are fused via a gating mechanism and residual connection, enhancing representational capacity while preserving gradient stability. Experimental results on the CIFAR-10 dataset demonstrate that SGFA-ConvNeXt improves classification accuracy by over 2% compared to the ConvNeXt-Tiny baseline, with only a marginal increase in FLOPs. Moreover, it shows competitive performance among various advanced CNN architectures. Ablation studies further validate the complementary nature of the spatial and channel attention paths in SGFA, underscoring its effectiveness in performance enhancement under low computational cost. This method offers a novel design strategy for efficient image classification in resource-constrained scenarios