Methods › Computer Vision › Feature Extractors › SFAM
Scale-wise Feature Aggregation Module
SFAM
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
SFAM, or Scale-wise Feature Aggregation Module, is a feature extraction block from the M2Det architecture. It aims to aggregate the multi-level multi-scale features generated by Thinned U-Shaped Modules into a multi-level feature pyramid.
The first stage of SFAM is to concatenate features of the equivalent scale together along the channel dimension. The aggregated feature pyramid can be presented as 𝐗 =[𝐗₁,𝐗₂,…,𝐗ᵢ], where 𝐗ᵢ = Concat(𝐱ᵢ¹,𝐱ᵢ²,…,𝐱ᵢᴸ) ∈ℝ^(Wᵢ×Hᵢ×C) refers to the features of the i-th largest scale. Here, each scale in the aggregated pyramid contains features from multi-level depths.
However, simple concatenation operations are not adaptive enough. In the second stage, we introduce a channel-wise attention module to encourage features to focus on channels that they benefit most. Following Squeeze-and-Excitation, we use global average pooling to generate channel-wise statistics 𝐳 ∈ℝ^C at the squeeze step. And to fully capture channel-wise dependencies, the following excitation step learns the attention mechanism via two fully connected layers:
𝐬 = 𝐅ₑₓ(𝐳,𝐖) = σ(𝐖₂ δ(𝐖₁𝐳)),
where σ refers to the ReLU function, δ refers to the sigmoid function, 𝐖₁ ∈ℝ^(C/r×C) , 𝐖₂ ∈ℝ^(C×C/r), r is the reduction ratio (r=16 in our experiments). The final output is obtained by reweighting the input 𝐗 with activation 𝐬:
𝐗̃ᵢᶜ = 𝐅_(scale)(𝐗ᵢᶜ,s_c) = s_c ·𝐗ᵢᶜ,
where 𝐗̃ᵢ̃ = [𝐗̃ᵢ¹,𝐗̃ᵢ²,...,𝐗̃ᵢ^C], each of the features is enhanced or weakened by the rescaling operation.
Papers archive 2025-07-28
5 shown of 5, 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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Emphasizing Crucial Features for Efficient Image Restoration 19 May 2024 · 1 repository · arXiv:2405.11468
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Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution 24 Jan 2021 · 1 repository · arXiv:2102.06732
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Structure from Articulated Motion: Accurate and Stable Monocular 3D Reconstruction without Training Data 12 May 2019 · 0 repositories · arXiv:1905.04789
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Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy 12 Nov 2018 · 0 repositories · arXiv:1811.04539
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M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network 12 Nov 2018 · 11 repositories · arXiv:1811.04533Syntology ran 1 of 1 samples · 0 unverified
Tasks archive 2025-07-28
13 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| 3D Feature Matching | 1 |
| 3D Reconstruction | 1 |
| Anomaly Detection | 1 |
| Decoder | 1 |
| Generative Adversarial Network | 1 |
| Image Restoration | 1 |
| Object | 1 |
| Object Detection | 1 |
| Text Detection | 1 |
| Text Spotting | 1 |
| Video Prediction | 1 |
| document understanding | 1 |
| object-detection | 1 |
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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