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 $\mathbf{k}_p \in \mathbb{R}^{3D \times 1 \times 1}$ and performs $3D^2HW$ operations to combine dimension-wise representations of $\mathbf{Y_{Dim}} \in \mathbb{R}^{3D \times H \times W}$ and produce an output $\mathbf{Y} \in \mathbb{R}^{D \times H \times W}$. This is computationally expensive. Dimension-wise fusion is an alternative that can allow us to combine representations of $\mathbf{Y_{Dim}}$ 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.
Source: DiCENet: Dimension-wise Convolutions for Efficient NetworksPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Image Classification | 1 | 20.00% |
Object Detection | 1 | 20.00% |
Real-Time Object Detection | 1 | 20.00% |
Real-Time Semantic Segmentation | 1 | 20.00% |
Semantic Segmentation | 1 | 20.00% |
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Average Pooling
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Pooling Operations | |
Dense Connections
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Feedforward Networks | |
Depthwise Convolution
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Convolutions | |
Grouped Convolution
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Convolutions | |
Sigmoid Activation
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Activation Functions |