Methods › General › Attention Mechanisms › GSoP-Net
Global second-order pooling convolutional networks
GSoP-Net
Introduced by Zilin Gao et al. in Global Second-order Pooling Convolutional Networks
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
A Gsop block has a squeeze module and an excitation module, and uses a second-order pooling to model high-order statistics while gathering global information. In the squeeze module, a GSoP block firstly reduces the number of channels from c to c′ (c′ < c) using a 1 ×1 convolution, then computes a c′ ×c′ covariance matrix for the different channels to obtain their correlation. Next, row-wise normalization is performed on the covariance matrix. Each (i, j) in the normalized covariance matrix explicitly relates channel i to channel j.
In the excitation module, a GSoP block performs row-wise convolution to maintain structural information and output a vector. Then a fully-connected layer and a sigmoid function are applied to get a c-dimensional attention vector. Finally, it multiplies the input features by the attention vector, as in an SE block. A GSoP block can be formulated as: s = F_(gsop)(X, θ) = σ(W RC(Cov(Conv(X)))) Y = s X Here, Conv(·) reduces the number of channels, Cov(·) computes the covariance matrix and RC(·) means row-wise convolution.
Papers archive 2025-07-28
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Global Second-order Pooling Convolutional Networks 29 Nov 2018 · 1 repository · arXiv:1811.12006Syntology ran 2 of 3 samples · 1 unverified · 1 pointer-only (licence)
Tasks archive 2025-07-28
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| Task | Papers |
|---|---|
| Object Recognition | 1 |
Usage over time archive 2025-07-28
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