BAM (Bottleneck Attention Module) is a parallel dual attention mechanism that computes channel and spatial attention maps simultaneously — then combines them with an element-wise addition, applied at bottleneck points between stages of a CNN.

How Does BAM Work?

• Channel Branch: Global average pooling -> MLP -> channel attention vector.
• Spatial Branch: 1×1 conv (reduce channels) -> dilated convolutions -> 1×1 conv -> spatial attention map.
• Combination: $M(F) = sigma(M_c(F) + M_s(F))$ (element-wise addition, then sigmoid).
• Placement: Between CNN stages (e.g., between ResNet stages), not within each block.
• Paper: Park et al. (2018).

Why It Matters

• Stage-Level Attention: Applied between stages rather than within every block -> lower total overhead.
• Parallel Processing: Channel and spatial branches computed in parallel (unlike CBAM's sequential approach).
• Complementary to CBAM: BAM for between-stage attention, CBAM for within-block attention.

BAM is the bottleneck attention gate — a dual-branch attention module placed at the transition points between CNN stages.