FcaNet (Frequency Channel Attention Network) is a channel attention mechanism that replaces global average pooling with DCT (Discrete Cosine Transform) frequency components — capturing richer channel statistics by using multiple frequency bases instead of just the DC (mean) component.

How Does FcaNet Work?

• Key Insight: Global average pooling = DC component of DCT. This captures only the mean and discards all frequency information.
• Multi-Frequency: Use different DCT frequency components for different channel groups (low, mid, high frequencies).
• Channel Split: Divide channels into groups, each processed with a different DCT basis.
• Attention: Generate attention weights from the multi-frequency representation via FC + sigmoid.
• Paper: Qin et al. (2021).

Why It Matters

• Richer Statistics: Captures frequency information beyond just the spatial mean (edges, textures, patterns).
• Drop-In: Replaces GAP in any SE-style attention module with no architectural changes.
• Improvement: Consistently outperforms SE-Net by using richer channel descriptors.

FcaNet is SE-Net with frequency vision — replacing the simple mean pooling with multi-frequency DCT components for richer channel attention.