Methods › General › Attention Mechanisms › Self-Calibrated Convolutions
Self-Calibrated Convolutions
Introduced by Jiang-Jiang Liu et al. in Improving Convolutional Networks With Self-Calibrated Convolutions
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Liu et al. presented self-calibrated convolution as a means to enlarge the receptive field at each spatial location.
Self-calibrated convolution is used together with a standard convolution. It first divides the input feature X into X₁ and X₂ in the channel domain. The self-calibrated convolution first uses average pooling to reduce the input size and enlarge the receptive field: T₁ = AvgPoolᵣ(X₁) where r is the filter size and stride. Then a convolution is used to model the channel relationship and a bilinear interpolation operator Up is used to upsample the feature map:
X′₁ = Up(Conv₂(T₁))
Next, element-wise multiplication finishes the self-calibrated process:
Y′₁ = Conv₃(X₁) σ(X₁ + X′₁)
Finally, the output feature map of is formed: Y₁ = Conv₄(Y′₁) Y₂ = Conv₁(X₂) Y = [Y₁; Y₂] Such self-calibrated convolution can enlarge the receptive field of a network and improve its adaptability. It achieves excellent results in image classification and certain downstream tasks such as instance segmentation, object detection and keypoint detection.
Papers archive 2025-07-28
2 shown of 2, 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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Self-calibrated convolution towards glioma segmentation 7 Feb 2024 · 0 repositories · arXiv:2402.05218
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Improving Convolutional Networks With Self-Calibrated Convolutions 1 Jun 2020 · 3 repositories
Tasks archive 2025-07-28
9 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Brain Tumor Segmentation | 1 |
| Instance Segmentation | 1 |
| Keypoint Detection | 1 |
| Object Detection | 1 |
| Representation Learning | 1 |
| Segmentation | 1 |
| Semantic Segmentation | 1 |
| Tumor Segmentation | 1 |
| object-detection | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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