bam

**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.