Involution: Inverting the Inherence of Convolution for Visual Recognition
ByteDance AI Lab

Convolution has been the core ingredient of modern neural networks. Now authors propose a novel atomic operation or deep neural networks by inverting the design principles of convolution.

Proposed Involution-based models improve over the conv-based baselines using ResNet-50:
- by up to 1.6% top-1 accuracy on Imagent classification,
- by 2.5% detection AP on COCO and
- by 2.4% on COCO segmentation
- by 4.7% mean IoU on Cityscapes segmentation
Moreover, the computational cost is reduced by ~60%.

To understand the Involution, it's better to read the paper though.
I don't know but maybe it will be something that universal like GroupNorm and will improve performance in almost any task?

📝 Paper
🛠 Code