Methods › General › Attention Mechanisms › GALA
Global-and-Local attention
GALA
Introduced by Drew Linsley et al. in Learning what and where to attend
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Most attention mechanisms learn where to focus using only weak supervisory signals from class labels, which inspired Linsley et al. to investigate how explicit human supervision can affect the performance and interpretability of attention models. As a proof of concept, Linsley et al. proposed the global-and-local attention (GALA) module, which extends an SE block with a spatial attention mechanism.
Given the input feature map X, GALA uses an attention mask that combines global and local attention to tell the network where and on what to focus. As in SE blocks, global attention aggregates global information by global average pooling and then produces a channel-wise attention weight vector using a multilayer perceptron. In local attention, two consecutive 1×1 convolutions are conducted on the input to produce a positional weight map. The outputs of the local and global pathways are combined by addition and multiplication. Formally, GALA can be represented as: s_g = W₂ δ(W₁GAP(x))
sₗ = Conv₂^(1×1) (δ(Conv₁^(1×1)(X)))
s_g^* = Expand(s_g)
sₗ^* = Expand(sₗ)
s = tanh(a(s_g^* + sₗ^*) +m ·(s_g^* sₗ^*) )
Y = sX
where a,m ∈ℝ^C are learnable parameters representing channel-wise weight vectors.
Supervised by human-provided feature importance maps, GALA has significantly improved representational power and can be combined with any CNN backbone.
Papers archive 2025-07-28
9 shown of 9, 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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Online Learning-guided Learning Rate Adaptation via Gradient Alignment 10 Jun 2025 · 0 repositories · arXiv:2506.08419
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GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation 22 Oct 2024 · 1 repository · arXiv:2410.16606
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GALA: Geometry-Aware Local Adaptive Grids for Detailed 3D Generation 13 Oct 2024 · 0 repositories · arXiv:2410.10037
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GALA: Generating Animatable Layered Assets from a Single Scan 23 Jan 2024 · 0 repositories · arXiv:2401.12979
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Does Invariant Graph Learning via Environment Augmentation Learn Invariance? 29 Oct 2023 · 1 repository · arXiv:2310.19035Syntology ran 3 of 3 samples · 0 unverified
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Temporal and Contextual Transformer for Multi-Camera Editing of TV Shows 17 Oct 2022 · 0 repositories · arXiv:2210.08737
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GALA: Toward Geometry-and-Lighting-Aware Object Search for Compositing 31 Mar 2022 · 0 repositories · arXiv:2204.00125
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Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning 9 Jun 2019 · 1 repository · arXiv:1906.04585
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Learning what and where to attend 22 May 2018 · 1 repository · arXiv:1805.08819
Tasks archive 2025-07-28
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Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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