Methods › General › Attention › Quick Attention
Quick Attention
Introduced by Saad Wazir et al. in HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
QA( x ) = σ( f( x )¹ˣ¹ ) + x
Quick Attention takes in the feature map as an input WxHxC (Width x Height x Channels) and creates two instances of the input feature map then it performs the 1x1xC convolution on the first instance and calculates the sigmoid activations after that it is added with the second instance to generate the final attention map as output which is of same dimensions as of input.
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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HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images 1 Sep 2022 · 1 repository · arXiv:2209.00729
Tasks archive 2025-07-28
6 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 |
|---|---|
| Boundary Detection | 1 |
| Decoder | 1 |
| Image Segmentation | 1 |
| Medical Image Segmentation | 1 |
| Segmentation | 1 |
| Semantic Segmentation | 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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections