ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer

TL;DR

ParC-Net is a novel ConvNet backbone that integrates vision transformer merits through position aware circular convolution, achieving superior accuracy, fewer parameters, and faster inference on various vision tasks.

Contribution

It introduces ParC, a lightweight, position-sensitive circular convolution, and a meta-former block that combines convolution and attention, enhancing ConvNets with transformer-like capabilities.

Findings

ParC-Net outperforms lightweight ConvNets and vision transformers in accuracy and speed.

Achieves 78.6% top-1 accuracy on ImageNet-1k with fewer parameters.

Demonstrates superior performance on object detection and segmentation tasks.

Abstract

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