Convolution has been the core ingredient of modern neural networks,
triggering the surge of deep learning in vision. 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 popular self-attention operator and subsume it into our involution
family as an over-complicated instantiation. The proposed involution operator
could be leveraged as fundamental bricks to build the new generation of neural
networks for visual recognition, powering different deep learning models on
several prevalent benchmarks, including ImageNet 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
absolutely 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 this https URL

本文提出一种新的深度神经网络操作 Atomic Involution，其将传统卷积的空间不可知和通道特异性原则进行了倒置，可作为构建新一代神经网络的基础砖块并提高了卷积基线的性能。