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style-based recalibration module

SRM

33 papers tagged archive 2025-07-28

Introduced by HyunJae Lee et al. in SRM: A Style-Based Recalibration Module for Convolutional Neural Networks

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

SRM combines style transfer with an attention mechanism. Its main contribution is style pooling which utilizes both mean and standard deviation of the input features to improve its capability to capture global information. It also adopts a lightweight channel-wise fully-connected (CFC) layer, in place of the original fully-connected layer, to reduce the computational requirements. Given an input feature map X ∈ℝ^(C ×H ×W), SRM first collects global information by using style pooling (SP(·)) which combines global average pooling and global standard deviation pooling. Then a channel-wise fully connected (CFC(·)) layer (i.e. fully connected per channel), batch normalization BN and sigmoid function σ are used to provide the attention vector. Finally, as in an SE block, the input features are multiplied by the attention vector. Overall, an SRM can be written as: s = Fₛᵣₘ(X, θ) = σ(BN(CFC(SP(X)))) Y = s X The SRM block improves both squeeze and excitation modules, yet can be added after each residual unit like an SE block.

PaperSource

Papers archive 2025-07-28

30 shown of 33, 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 26 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
Computational Efficiency3
Decision Making3
Model Selection2
Segmentation2
Semantic Segmentation2
Autonomous Vehicles1
Collaborative Inference1
Deep Learning1
Device-Cloud Collaboration1
Distributional Reinforcement Learning1
Edge-computing1
Experimental Design1
Feature Importance1
In-Context Learning1
Knowledge Distillation1
Language Modeling1
Language Modelling1
Large Language Model1
Mathematical Reasoning1
Numerical Integration1

Usage over time archive 2025-07-28

Papers per year tagged with SRM: 2019 to 2025, peak 8 8 0 2019: 5 papers 2019 2020: 2 papers 2020 2021: 3 papers 2021 2022: 5 papers 2022 2023: 5 papers 2023 2024: 8 papers 2024 2025: 5 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (33 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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