Methods › General › Attention Modules › Attention-augmented Convolution
Attention-augmented Convolution
Introduced by Irwan Bello et al. in Attention Augmented Convolutional Networks
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Attention-augmented Convolution is a type of convolution with a two-dimensional relative self-attention mechanism that can replace convolutions as a stand-alone computational primitive for image classification. It employs scaled-dot product attention and multi-head attention as with Transformers.
It works by concatenating convolutional and attentional feature map. To see this, consider an original convolution operator with kernel size k, Fᵢₙ input filters and Fₒᵤₜ output filters. The corresponding attention augmented convolution can be written as"
AAConv(X) = Concat[Conv(X), MHA(X)]
X originates from an input tensor of shape (H, W, Fᵢₙ). This is flattened to become X ∈ℝ^(HW ×Fᵢₙ) which is passed into a multi-head attention module, as well as a convolution (see above).
Similarly to the convolution, the attention augmented convolution 1) is equivariant to translation and 2) can readily operate on inputs of different spatial dimensions.
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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RISurConv: Rotation Invariant Surface Attention-Augmented Convolutions for 3D Point Cloud Classification and Segmentation 12 Aug 2024 · 1 repository · arXiv:2408.06110Syntology ran 11 of 16 samples · 5 unverified
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MuSLCAT: Multi-Scale Multi-Level Convolutional Attention Transformer for Discriminative Music Modeling on Raw Waveforms 6 Apr 2021 · 0 repositories · arXiv:2104.02309
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Attention Augmented Convolutional Networks 22 Apr 2019 · 14 repositories · arXiv:1904.09925Syntology ran 3 of 6 samples · 3 unverified · 1 pointer-only (licence)
Tasks archive 2025-07-28
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