Methods › General › Attention Mechanisms › GCT
Gated Channel Transformation
GCT
Introduced by Zongxin Yang et al. in Gated Channel Transformation for Visual Recognition
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
GCT first collects global information by computing the l2-norm of each channel. Next, a learnable vector α is applied to scale the feature. Then a competition mechanism is adopted by channel normalization to interact between channels.
Unlike previous methods, GCT first collects global information by computing the l₂-norm of each channel. Next, a learnable vector α is applied to scale the feature. Then a competition mechanism is adopted by channel normalization to interact between channels. Like other common normalization methods, a learnable scale parameter γ and bias β are applied to rescale the normalization. However, unlike previous methods, GCT adopts tanh activation to control the attention vector. Finally, it not only multiplies the input by the attention vector but also adds an identity connection. GCT can be written as: s = F_(gct)(X, θ) = tanh(γCN(αNorm(X)) + β) Y = s X + X
where α, β and γ are trainable parameters. Norm(·) indicates the L2-norm of each channel. CN is channel normalization.
A GCT block has fewer parameters than an SE block, and as it is lightweight, can be added after each convolutional layer of a CNN.
Papers archive 2025-07-28
8 shown of 8, 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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STEAM: Squeeze and Transform Enhanced Attention Module 12 Dec 2024 · 0 repositories · arXiv:2412.09023
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Generative causal testing to bridge data-driven models and scientific theories in language neuroscience 1 Oct 2024 · 1 repository · arXiv:2410.00812Syntology ran 8 of 11 samples · 3 unverified
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TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling 6 Jan 2024 · 0 repositories · arXiv:2401.03138Syntology ran 2 of 7 samples · 5 unverified · 7 pointer-only (licence)
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Granger Causality for Predictability in Dynamic Mode Decomposition 23 Oct 2022 · 0 repositories · arXiv:2210.12737
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Generalised Co-Salient Object Detection 20 Aug 2022 · 0 repositories · arXiv:2208.09668
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GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning 15 Mar 2022 · 0 repositories · arXiv:2203.07738
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Integrating Fréchet distance and AI reveals the evolutionary trajectory and origin of SARS-CoV-2 14 Oct 2021 · 0 repositories · arXiv:2110.07696
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Gated Channel Transformation for Visual Recognition 25 Sep 2019 · 3 repositories · arXiv:1909.11519
Tasks archive 2025-07-28
19 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
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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