Methods › Computer Vision › Pooling Operations › Local Importance-based Pooling
Local Importance-based Pooling
Introduced by Ziteng Gao et al. in LIP: Local Importance-based Pooling
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Local Importance-based Pooling (LIP) is a pooling layer that can enhance discriminative features during the downsampling procedure by learning adaptive importance weights based on inputs. By using a learnable network G in F, the importance function now is not limited in hand-crafted forms and able to learn the criterion for the discriminativeness of features. Also, the window size of LIP is restricted to be not less than stride to fully utilize the feature map and avoid the issue of fixed interval sampling scheme. More specifically, the importance function in LIP is implemented by a tiny fully convolutional network, which learns to produce the importance map based on inputs in an end-to-end manner.
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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LIP: Local Importance-based Pooling 12 Aug 2019 · 1 repository · arXiv:1908.04156
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 |
| Object Detection | 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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