Methods › Computer Vision › Image Model Blocks › Spatial Group-wise Enhance
Spatial Group-wise Enhance
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Spatial Group-wise Enhance is a module for convolutional neural networks that can adjust the importance of each sub-feature by generating an attention factor for each spatial location in each semantic group, so that every individual group can autonomously enhance its learnt expression and suppress possible noise
Inside each feature group, we model a spatial enhance mechanism inside each feature group, by scaling the feature vectors over all the locations with an attention mask. This attention mask is designed to suppress the possible noise and highlight the correct semantic feature regions. Different from other popular attention methods, it utilises the similarity between the global statistical feature and the local ones of each location as the source of generation for the attention masks.
Papers archive 2025-07-28
1 shown of 1, 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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Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks 23 May 2019 · 3 repositories · arXiv:1905.09646Syntology ran 2 of 4 samples · 2 unverified · 4 pointer-only (licence)
Tasks archive 2025-07-28
2 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 |
|---|---|
| Image Classification | 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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