Involution: Inverting the Inherence of Convolution for Visual Recognition

Convolution has been the core ingredient of modern neu- ral networks, triggering the surge of deep learning in vi- sion. In this work, we rethink the inherent principles of standard convolution for vision tasks, specifically spatial- agnostic and channel-specific. Instead, we present a novel atomic operation for deep neural networks by inverting the aforementioned design principles of convolution, coined as involution. We additionally demystify the recent pop- ular self-attention operator and subsume it into our invo- lution family as an over-complicated instantiation. The proposed involution operator could be leveraged as fun- damental bricks to build the new generation of neural net- works for visual recognition, powering different deep learn- ing models on several prevalent benchmarks, including Im- ageNet classification, COCO detection and segmentation, together with Cityscapes segmentation. Our involution- based models improve the performance of convolutional baselines using ResNet-50 by up to 1.6% top-1 accuracy, 2.5% and 2.4% bounding box AP, and 4.7% mean IoU ab- solutely while compressing the computational cost to 66%, 65%, 72%, and 57% on the above benchmarks, respectively. Code and pre-trained models for all the tasks are available at https://github.com/d-li14/involution.