DMSCA: Dynamic Multi-Scale Channel-Spatial Attention for Enhanced Feature Representation in Convolutional Neural Networks

The attention mechanism improves convolutional neural networks (CNNs) by emphasizing important features, yet current approaches often lack in capturing multi-scale contexts, deeply integrating channel and spatial information, and adapting dynamically. We introduce the Dynamic Multi-Scale Channel-Spatial Attention (DMSCA) mechanism, which synergistically combines multi-scale encoding, directional interactions, and adaptive activations for enhanced feature coupling. Unlike fixed-structure methods like CBAM, DMSCA employs learnable dynamic weights for adaptive channel-spatial fusion. Key innovations include Temperature-controlled Channel Attention (TCA) and Direction-aware Multi-scale Spatial Context Encoder (MSCE). DMSCA integrates six components: Global Context Encoder, TCA, MSCE, Directional Information Interaction, Dynamic Feature Fusion, and Adaptive Activation. Evaluations on CIFAR-10/100 and ImageNet demonstrate superior performance over state-of-the-art attentions, with a 1.52% Top-1 accuracy gain on ResNet-50 at modest computational cost (11.3% parameter and 2.4% FLOPs increase).