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.