Methods › Computer Vision › Vision Transformers › Twins-SVT
Twins-SVT
Introduced by Xiangxiang Chu et al. in Twins: Revisiting the Design of Spatial Attention in Vision Transformers
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Twins-SVT is a type of vision transformer which utilizes a spatially separable attention mechanism (SSAM) which is composed of two types of attention operations—(i) locally-grouped self-attention (LSA), and (ii) global sub-sampled attention (GSA), where LSA captures the fine-grained and short-distance information and GSA deals with the long-distance and global information. On top of this, it utilizes conditional position encodings as well as the architectural design of the Pyramid Vision Transformer.
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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Twins: Revisiting the Design of Spatial Attention in Vision Transformers 28 Apr 2021 · 9 repositories · arXiv:2104.13840Syntology ran 0 of 2 samples · 2 unverified · 2 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 |
| 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