Local Importance-based Pooling

Introduced by Gao et al. in LIP: Local Importance-based Pooling

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.

Source: LIP: Local Importance-based Pooling

Latest Papers

PAPER DATE
LIP: Local Importance-based Pooling
| Ziteng GaoLimin WangGangshan Wu
2019-08-12

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Image Classification 1 50.00%
Object Detection 1 50.00%

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