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Involution: Inverting the Inherence of Convolution for Visual RecognitionDuo Li1Jie Hu2Changhu Wang2Xiangtai Li3Qi She2Lei Zhu3Tong Zhang1Qifeng Chen1The Hong Kong University of Science and Technology1ByteDance AI Lab2Peking 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 ofstandard Convolution for vision tasks, specifically spatial-agnostic and channel-specific. Instead, we present a novelatomic operation for deep neural networks by invertingthe aforementioned design principles of Convolution , coinedas involution.

convolution through the lens of our involution. 3.The involution-powered architectures work universally well across a wide array of vision tasks, including im-age classification, object detection, instance and se-mantic segmentation, offering significantly better per-formance than the convolution-based counterparts. 2. Sketch of Convolution

  Convolutions

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