Methods › Computer Vision › Image Model Blocks › DimFuse
Dimension-wise Fusion
DimFuse
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Dimension-wise Fusion is an image model block that attempts to capture global information by combining features globally. It is an alternative to point-wise convolution. A point-wise convolutional layer applies D point-wise kernels 𝐤ₚ ∈ℝ^(3D ×1 ×1) and performs 3D²HW operations to combine dimension-wise representations of 𝐘_(𝐃𝐢𝐦) ∈ℝ^(3D ×H ×W) and produce an output 𝐘 ∈ℝ^(D ×H ×W). This is computationally expensive. Dimension-wise fusion is an alternative that can allow us to combine representations of 𝐘_(𝐃𝐢𝐦) efficiently. As illustrated in the Figure to the right, it factorizes the point-wise convolution in two steps: (1) local fusion and (2) global fusion.
Papers archive 2025-07-28
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DiCENet: Dimension-wise Convolutions for Efficient Networks 8 Jun 2019 · 2 repositories · arXiv:1906.03516Syntology ran 0 of 1 samples · 1 unverified
Tasks archive 2025-07-28
8 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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