Recently, some large kernel convnets strike back with appealing performance and efficiency. However, given the square complexity of convolution, scaling up kernels can bring about an enormous amount of parameters and the proliferated parameters can induce severe optimization problem. Due to these issues, current CNNs compromise to scale up to 51x51 in the form of stripe convolution (i.e., 51x5 + 5x51) and start to saturate as the kernel size continues growing. In this paper, we delve into addressing these vital issues and explore whether we can continue scaling up kernels for more performance gains. Inspired by human vision, we propose a human-like peripheral convolution that efficiently reduces over 90% parameter count of dense grid convolution through parameter sharing, and manage to scale up kernel size to extremely large. Our peripheral convolution behaves highly similar to human, reducing the complexity of convolution from O(K^2) to O(logK) without backfiring performance. Built on this, we propose Parameter-efficient Large Kernel Network (PeLK). Our PeLK outperforms modern vision Transformers and ConvNet architectures like Swin, ConvNeXt, RepLKNet and SLaK on various vision tasks including ImageNet classification, semantic segmentation on ADE20K and object detection on MS COCO. For the first time, we successfully scale up the kernel size of CNNs to an unprecedented 101x101 and demonstrate consistent improvements.

我们提出了一种人类视觉启发的周边卷积方法，通过参数共享高效地减少了密集网格卷积的参数数量超过90%，并且成功实现了卷积核大小的可扩展性。基于这种方法，我们提出了一种参数高效的大核心网络（PeLK），在各种视觉任务中，包括ImageNet分类、ADE20K的语义分割和MS COCO的目标检测，PeLK都优于现代视觉Transformer和ConvNet架构。我们首次将CNN的卷积核大小扩展到了101x101，并展示了持续的改进效果。

PeLK：具有外围卷积的参数高效大核心卷积神经网络