title: LSNet: See Large, Focus Small
summary: Vision network designs, including Convolutional Neural Networks and Vision
  Transformers, have significantly advanced the field of computer vision. Yet,
  their complex computations pose challenges for practical deployments,
  particularly in real-time applications. To tackle this issue, researchers have
  explored various lightweight and efficient network designs. However, existing
  lightweight models predominantly leverage self-attention mechanisms and
  convolutions for token mixing. This dependence brings limitations in
  effectiveness and efficiency in the perception and aggregation processes of
  lightweight networks, hindering the balance between performance and efficiency
  under limited computational budgets. In this paper, we draw inspiration from
  the dynamic heteroscale vision ability inherent in the efficient human vision
  system and propose a ``See Large, Focus Small'' strategy for lightweight vision
  network design. We introduce LS (Large-Small) convolution,
  which combines large-kernel perception and small-kernel aggregation. It can
  efficiently capture a wide range of perceptual information and achieve precise
  feature aggregation for dynamic and complex visual representations, thus
  enabling proficient processing of visual information. Based on LS convolution,
  we present LSNet, a new family of lightweight models. Extensive experiments
  demonstrate that LSNet achieves superior performance and efficiency over
  existing lightweight networks in various vision tasks. Codes and models are
  available at https://github.com/jameslahm/lsnet.
paper_authors._id: 67eb3d2110032c28d1ea109f
paper_authors.name: Ao Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-02T16:11:10.373Z
paper_authors._id: 628ece6054698ce61d1e7be3
paper_authors.avatarUrl: /avatars/c6ab33843fd0d8ef003650c1094214c0.svg
paper_authors.fullname: Ao Wang
paper_authors.type: user
paper_authors.user: jameslahm
paper_authors._id: 67eb3d2110032c28d1ea10a0
paper_authors.name: Hui Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-03T14:06:28.314Z
paper_authors._id: 670d30d232944974d65fdf47
paper_authors.avatarUrl: /avatars/193566fcdf5768512d17e4c1801adf45.svg
paper_authors.fullname: Hui Chen
paper_authors.type: user
paper_authors.user: huichen-cs
paper_authors._id: 67eb3d2110032c28d1ea10a1
paper_authors.name: Zijia Lin
paper_authors._id: 67eb3d2110032c28d1ea10a2
paper_authors.name: Jungong Han
paper_authors._id: 67eb3d2110032c28d1ea10a3
paper_authors.name: Guiguang Ding
paper_title: LSNet: See Large, Focus Small
paper_summary: Vision network designs, including Convolutional Neural Networks and Vision
  Transformers, have significantly advanced the field of computer vision. Yet,
  their complex computations pose challenges for practical deployments,
  particularly in real-time applications. To tackle this issue, researchers have
  explored various lightweight and efficient network designs. However, existing
  lightweight models predominantly leverage self-attention mechanisms and
  convolutions for token mixing. This dependence brings limitations in
  effectiveness and efficiency in the perception and aggregation processes of
  lightweight networks, hindering the balance between performance and efficiency
  under limited computational budgets. In this paper, we draw inspiration from
  the dynamic heteroscale vision ability inherent in the efficient human vision
  system and propose a ``See Large, Focus Small'' strategy for lightweight vision
  network design. We introduce LS (Large-Small) convolution,
  which combines large-kernel perception and small-kernel aggregation. It can
  efficiently capture a wide range of perceptual information and achieve precise
  feature aggregation for dynamic and complex visual representations, thus
  enabling proficient processing of visual information. Based on LS convolution,
  we present LSNet, a new family of lightweight models. Extensive experiments
  demonstrate that LSNet achieves superior performance and efficiency over
  existing lightweight networks in various vision tasks. Codes and models are
  available at https://github.com/jameslahm/lsnet.
paper_ai_summary: LSNet, utilizing a "See Large, Focus Small" strategy with LS convolution, improves the performance and efficiency of lightweight vision networks.
paper_ai_keywords: Convolutional Neural Networks Vision Transformers lightweight and efficient network designs self-attention mechanisms convolutions token mixing LS convolution large-kernel perception small-kernel aggregation perceptual information precise feature aggregation LSNet