LSNet: See Large, Focus Small
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.
67eb3d2110032c28d1ea109f
Ao Wang
admin_assigned
2025-04-02T16:11:10.373Z
628ece6054698ce61d1e7be3
/avatars/c6ab33843fd0d8ef003650c1094214c0.svg
Ao Wang
user
jameslahm
67eb3d2110032c28d1ea10a0
Hui Chen
admin_assigned
2025-04-03T14:06:28.314Z
670d30d232944974d65fdf47
/avatars/193566fcdf5768512d17e4c1801adf45.svg
Hui Chen
user
huichen-cs
67eb3d2110032c28d1ea10a1
Zijia Lin
67eb3d2110032c28d1ea10a2
Jungong Han
67eb3d2110032c28d1ea10a3
Guiguang Ding
LSNet: See Large, Focus Small
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.
LSNet, utilizing a "See Large, Focus Small" strategy with LS convolution, improves the performance and efficiency of lightweight vision networks.
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