Cbam

CBAM (Convolutional Block Attention Module) is a dual attention mechanism that applies both channel attention and spatial attention sequentially — first recalibrating "what" features are important (channel), then "where" they are important (spatial).

How Does CBAM Work?

• Channel Attention: Like SE but uses both global avg pooling and max pooling: $M_c = \sigma(MLP(AvgPool(F)) + MLP(MaxPool(F)))$.
• Spatial Attention: $M_s = \sigma(Conv([AvgPool_c(F'); MaxPool_c(F')]))$ — 7×7 conv on channel-pooled features.
• Sequential: Channel attention first, then spatial attention: $F'' = M_s \otimes (M_c \otimes F)$.
• Paper: Woo et al. (2018).

Why It Matters

• Complementary: Channel attention (what) + spatial attention (where) captures richer information than either alone.
• Lightweight: Small computational overhead for consistent accuracy improvement.
• Plug-and-Play: Can be inserted into any CNN architecture at any stage.

CBAM is the "what" and "where" attention module — teaching networks to focus on the right features in the right locations.