  Recently, vision transformers started to show impressive results which
outperform large convolution based models significantly. However, in the area
of small models for mobile or resource constrained devices, ConvNet still has
its own advantages in both performance and model complexity. We propose
ParC-Net, a pure ConvNet based backbone model that further strengthens these
advantages by fusing the merits of vision transformers into ConvNets.
Specifically, we propose position aware circular convolution (ParC), a
light-weight convolution op which boasts a global receptive field while
producing location sensitive features as in local convolutions. We combine the
ParCs and squeeze-exictation ops to form a meta-former like model block, which
further has the attention mechanism like transformers. The aforementioned block
can be used in plug-and-play manner to replace relevant blocks in ConvNets or
transformers. Experiment results show that the proposed ParC-Net achieves
better performance than popular light-weight ConvNets and vision transformer
based models in common vision tasks and datasets, while having fewer parameters
and faster inference speed. For classification on ImageNet-1k, ParC-Net
achieves 78.6% top-1 accuracy with about 5.0 million parameters, saving 11%
parameters and 13% computational cost but gaining 0.2% higher accuracy and 23%
faster inference speed (on ARM based Rockchip RK3288) compared with MobileViT,
and uses only 0.5 times parameters but gaining 2.7% accuracy compared with
DeIT. On MS-COCO object detection and PASCAL VOC segmentation tasks, ParC-Net
also shows better performance. Source code is available at
https://github.com/hkzhang91/ParC-Net
