title: Scaling Up Your Kernels: Large Kernel Design in ConvNets towards
    Universal Representations
summary: This paper proposes the paradigm of large convolutional kernels in designing
  modern Convolutional Neural Networks (ConvNets). We establish that employing a
  few large kernels, instead of stacking multiple smaller ones, can be a superior
  design strategy. Our work introduces a set of architecture design guidelines
  for large-kernel ConvNets that optimize their efficiency and performance. We
  propose the UniRepLKNet architecture, which offers systematical architecture
  design principles specifically crafted for large-kernel ConvNets, emphasizing
  their unique ability to capture extensive spatial information without deep
  layer stacking. This results in a model that not only surpasses its
  predecessors with an ImageNet accuracy of 88.0%, an ADE20K mIoU of 55.6%, and a
  COCO box AP of 56.4% but also demonstrates impressive scalability and
  performance on various modalities such as time-series forecasting, audio, point
  cloud, and video recognition. These results indicate the universal modeling
  abilities of large-kernel ConvNets with faster inference speed compared with
  vision transformers. Our findings reveal that large-kernel ConvNets possess
  larger effective receptive fields and a higher shape bias, moving away from the
  texture bias typical of smaller-kernel CNNs. All codes and models are publicly
  available at https://github.com/AILab-CVC/UniRepLKNet promoting further
  research and development in the community.
paper_authors._id: 67089917e0c87dc0e549d3b4
paper_authors.name: Yiyuan Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-11T11:05:59.964Z
paper_authors._id: 63176933b58b0184630d2c74
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63176933b58b0184630d2c74/53b5EASwW76zeyyqeJA3O.jpeg
paper_authors.fullname: Yiyuan Zhang
paper_authors.type: user
paper_authors.user: Yiyuan
paper_authors._id: 67089917e0c87dc0e549d3b5
paper_authors.name: Xiaohan Ding
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-11T11:06:06.209Z
paper_authors._id: 6564f049957ac688429a6754
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/nE6q5TlyLKlJpbxCKoDN8.png
paper_authors.fullname: xiaohanding
paper_authors.type: user
paper_authors.user: DingXiaoH
paper_authors._id: 67089917e0c87dc0e549d3b6
paper_authors.name: Xiangyu Yue
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-11T11:06:13.181Z
paper_authors._id: 666a8f24e2990b0cb16b7bf9
paper_authors.avatarUrl: /avatars/fcbaf8f1e3e53a2a4a819b7cb2c53aa4.svg
paper_authors.fullname: Xiangyu Yue
paper_authors.type: user
paper_authors.user: xyyue
paper_title: Scaling Up Your Kernels: Large Kernel Design in ConvNets towards
    Universal Representations
paper_summary: This paper proposes the paradigm of large convolutional kernels in designing
  modern Convolutional Neural Networks (ConvNets). We establish that employing a
  few large kernels, instead of stacking multiple smaller ones, can be a superior
  design strategy. Our work introduces a set of architecture design guidelines
  for large-kernel ConvNets that optimize their efficiency and performance. We
  propose the UniRepLKNet architecture, which offers systematical architecture
  design principles specifically crafted for large-kernel ConvNets, emphasizing
  their unique ability to capture extensive spatial information without deep
  layer stacking. This results in a model that not only surpasses its
  predecessors with an ImageNet accuracy of 88.0%, an ADE20K mIoU of 55.6%, and a
  COCO box AP of 56.4% but also demonstrates impressive scalability and
  performance on various modalities such as time-series forecasting, audio, point
  cloud, and video recognition. These results indicate the universal modeling
  abilities of large-kernel ConvNets with faster inference speed compared with
  vision transformers. Our findings reveal that large-kernel ConvNets possess
  larger effective receptive fields and a higher shape bias, moving away from the
  texture bias typical of smaller-kernel CNNs. All codes and models are publicly
  available at https://github.com/AILab-CVC/UniRepLKNet promoting further
  research and development in the community.
paper_ai_keywords: Convolutional Neural Networks (ConvNets) large convolutional kernels self-employing large kernels architecture design guidelines UniRepLKNet large-kernel ConvNets ImageNet accuracy ADE20K mIoU COCO box AP time-series forecasting audio point cloud video recognition effective receptive fields shape bias smaller-kernel CNNs vision transformers faster inference speed