Methods › Computer Vision › Convolutional Neural Networks › SKNet
SKNet
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
SKNet is a type of convolutional neural network that employs selective kernel units, with selective kernel convolutions, in its architecture. This allows for a type of attention where the network can learn to attend to different receptive fields.
Papers archive 2025-07-28
4 shown of 4, 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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Pick-or-Mix: Dynamic Channel Sampling for ConvNets 16 Jun 2024 · 1 repository · arXiv:2406.10935
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CTooth: A Fully Annotated 3D Dataset and Benchmark for Tooth Volume Segmentation on Cone Beam Computed Tomography Images 17 Jun 2022 · 1 repository · arXiv:2206.08778
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Attribute-Based Progressive Fusion Network for RGBT Tracking 26 Jan 2022 · 2 repositories
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DRPN: Making CNN Dynamically Handle Scale Variation 21 Dec 2021 · 0 repositories · arXiv:2112.10963
Tasks archive 2025-07-28
3 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 |
|---|---|
| Attribute | 1 |
| Rgb-T Tracking | 1 |
| 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