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Coordinate attention

31 papers tagged archive 2025-07-28

Introduced by Qibin Hou et al. in Coordinate Attention for Efficient Mobile Network Design

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Hou et al. proposed coordinate attention, a novel attention mechanism which embeds positional information into channel attention, so that the network can focus on large important regions at little computational cost.

The coordinate attention mechanism has two consecutive steps, coordinate information embedding and coordinate attention generation. First, two spatial extents of pooling kernels encode each channel horizontally and vertically. In the second step, a shared 1×1 convolutional transformation function is applied to the concatenated outputs of the two pooling layers. Then coordinate attention splits the resulting tensor into two separate tensors to yield attention vectors with the same number of channels for horizontal and vertical coordinates of the input X along. This can be written as zʰ = GAPʰ(X) zʷ = GAPʷ(X) f = δ(BN(Conv₁^(1×1)([zʰ;zʷ]))) fʰ, fʷ = Split(f) sʰ = σ(Convₕ^(1×1)(fʰ)) sʷ = σ(Conv_w^(1×1)(fʷ)) Y = X sʰ sʷ where GAPʰ and GAPʷ denote pooling functions for vertical and horizontal coordinates, and sʰ ∈ℝ^(C×1×W) and sʷ ∈ℝ^(C×H×1) represent corresponding attention weights.

Using coordinate attention, the network can accurately obtain the position of a targeted object. This approach has a larger receptive field than BAM and CBAM. Like an SE block, it also models cross-channel relationships, effectively enhancing the expressive power of the learned features. Due to its lightweight design and flexibility, it can be easily used in classical building blocks of mobile networks.

PaperSource

Papers archive 2025-07-28

30 shown of 31, 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.

Tasks archive 2025-07-28

20 shown of 51 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Semantic Segmentation6
Data Augmentation4
Object Detection4
Image Segmentation3
Segmentation3
object-detection3
Autonomous Driving2
Deep Learning2
Generative Adversarial Network2
Mamba2
Road Damage Detection2
3D Face Alignment1
Autonomous Vehicles1
Brain Tumor Segmentation1
CT Reconstruction1
Classification1
Colorization1
Computed Tomography (CT)1
Contrastive Learning1
Deep Attention1

Usage over time archive 2025-07-28

Papers per year tagged with Coordinate attention: 2021 to 2025, peak 10 10 0 2021: 3 papers 2021 2022: 8 papers 2022 2023: 7 papers 2023 2024: 10 papers 2024 2025: 3 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (31 dated). Bars are counts, not a trend claim.

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

Attention Mechanisms

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