Methods › General › Attention Modules › CAB
Contextual Attention Block
CAB
Introduced by Gianluca Carloni et al. in Connectivity-Inspired Network for Context-Aware Recognition
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
The Contextual Attention Block (CAB) is a new plug-and-play module to model context awareness. It is simple and effective and can be integrated with any feed-forward neural network.
CAB infers weights that multiply the feature maps according to their causal influence on the scene, modeling the co-occurrence of different objects in the image.
You can place the CAB module at different bottlenecks to infuse a hierarchical context awareness into the model.
Papers archive 2025-07-28
2 shown of 2, 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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MSCA-Net:Multi-Scale Context Aggregation Network for Infrared Small Target Detection 21 Mar 2025 · 0 repositories · arXiv:2503.17193
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Connectivity-Inspired Network for Context-Aware Recognition 6 Sep 2024 · 1 repository · arXiv:2409.04360
Tasks archive 2025-07-28
5 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 |
|---|---|
| Biologically-plausible Training | 1 |
| Causal Discovery | 1 |
| Functional Connectivity | 1 |
| Image Classification | 1 |
| image-classification | 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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