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efficient channel attention

ECANet

29 papers tagged archive 2025-07-28

Introduced by Qilong Wang et al. in ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

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

An ECA block has similar formulation to an SE block including a squeeze module for aggregating global spatial information and an efficient excitation module for modeling cross-channel interaction. Instead of indirect correspondence, an ECA block only considers direct interaction between each channel and its k-nearest neighbors to control model complexity. Overall, the formulation of an ECA block is: s = F_(eca)(X, θ) = σ(Conv1D(GAP(X))) Y = s X where Conv1D(·) denotes 1D convolution with a kernel of shape k across the channel domain, to model local cross-channel interaction. The parameter k decides the coverage of interaction, and in ECA the kernel size k is adaptively determined from the channel dimensionality C instead of by manual tuning, using cross-validation: k = ψ(C) = | (log₂(C))/γ+b/γ|_(odd)

where γ and b are hyperparameters. |x|_(odd) indicates the nearest odd function of x.

Compared to SENet, ECANet has an improved excitation module, and provides an efficient and effective block which can readily be incorporated into various CNNs.

PaperSource

Papers archive 2025-07-28

29 shown of 29, 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 65 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
Object Detection7
object-detection5
Defect Detection3
Image Classification3
image-classification3
Classification2
Diagnostic2
Emotion Recognition2
Image Super-Resolution2
Instance Segmentation2
Insulator Defect Detection2
Knowledge Distillation2
Mamba2
Object2
Semantic Segmentation2
Super-Resolution2
Benchmarking1
Binary Classification1
Brain Tumor Segmentation1
Continual Learning1

Usage over time archive 2025-07-28

Papers per year tagged with ECANet: 2019 to 2025, peak 14 14 0 2019: 1 paper 2019 2020: 4 papers 2020 2021: 3 papers 2021 2022: 3 papers 2022 2023: 1 paper 2023 2024: 14 papers 2024 2025: 3 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (29 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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