Fig. 2 | Scientific Reports

Fig. 2

From: DMSCA: dynamic multi-scale channel-spatial attention for enhanced feature representation in convolutional neural networks

Fig. 2

Schematic illustration of the Global Context Encoder (GCE). The module processes the input feature \(\:\varvec{X}\) through parallel Global Average Pooling (GAP) and Global Max Pooling (GMP) streams. The resulting descriptors, \(\:{\varvec{X}}_{\varvec{a}\varvec{v}\varvec{g}}\) and \(\:{\varvec{X}}_{\varvec{m}\varvec{a}\varvec{x}}\), are fused via element-wise addition to preserve complementary spatial information. A shared MLP then projects this fused representation into the final global context descriptor \(\:{\varvec{F}}_{\varvec{g}\varvec{l}\varvec{o}\varvec{b}\varvec{a}\varvec{l}}\).

Back to article page