Methods › General › Attention Mechanisms › Attention Feature Filters
Attention Feature Filters
Introduced by Bandhav Veluri et al. in NeuriCam: Key-Frame Video Super-Resolution and Colorization for IoT Cameras
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
An attention mechanism for content-based filtering of multi-level features. For example, recurrent features obtained by forward and backward passes of a bidirectional RNN block can be combined using attention feature filters, with unprocessed input features/embeddings as queries and recurrent features as keys/values.
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.
-
NeuriCam: Key-Frame Video Super-Resolution and Colorization for IoT Cameras 25 Jul 2022 · 1 repository · arXiv:2207.12496
Tasks archive 2025-07-28
4 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 |
|---|---|
| Colorization | 1 |
| Key-Frame-based Video Super-Resolution (K = 15) | 1 |
| Super-Resolution | 1 |
| Video Super-Resolution | 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