  This paper does not attempt to design a state-of-the-art method for visual
recognition but investigates a more efficient way to make use of convolutions
to encode spatial features. By comparing the design principles of the recent
convolutional neural networks ConvNets) and Vision Transformers, we propose to
simplify the self-attention by leveraging a convolutional modulation operation.
We show that such a simple approach can better take advantage of the large
kernels (>=7x7) nested in convolutional layers. We build a family of
hierarchical ConvNets using the proposed convolutional modulation, termed
Conv2Former. Our network is simple and easy to follow. Experiments show that
our Conv2Former outperforms existent popular ConvNets and vision Transformers,
like Swin Transformer and ConvNeXt in all ImageNet classification, COCO object
detection and ADE20k semantic segmentation.
