Octave Convolution (OctConv) is a convolution operation that processes features at two spatial resolutions simultaneously — splitting feature maps into high-frequency (full resolution) and low-frequency (half resolution) components, reducing redundant spatial information.

How Does OctConv Work?

• Split: Divide channels into high-freq (H×W) and low-freq (H/2×W/2) groups.
• Four Paths: H→H (intra-high), L→L (intra-low), H→L (high-to-low downsample), L→H (low-to-high upsample).
• Ratio: $alpha$ controls the fraction of channels at low resolution (typically 0.5).
• Paper: Chen et al. (2019).

Why It Matters

• Efficiency: Low-freq features at half resolution -> significant FLOPs reduction (30-50%).
• Accuracy: Surprisingly, OctConv often improves accuracy while reducing compute (less spatial redundancy to overfit).
• Drop-In: Replaces standard convolution with minimal architectural changes.

OctConv is dual-resolution convolution — processing fine details at full resolution and coarse patterns at half resolution for efficiency and accuracy.