PANet

Introduced by Liu et al. in Path Aggregation Network for Instance Segmentation

Path Aggregation Network, or PANet, aims to boost information flow in a proposal-based instance segmentation framework. Specifically, the feature hierarchy is enhanced with accurate localization signals in lower layers by bottom-up path augmentation, which shortens the information path between lower layers and topmost feature. Additionally, adaptive feature pooling is employed, which links feature grid and all feature levels to make useful information in each feature level propagate directly to following proposal subnetworks. A complementary branch capturing different views for each proposal is created to further improve mask prediction.

Source: Path Aggregation Network for Instance Segmentation

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Papers

Paper	Code	Results	Date	Stars

Tasks

Task	Papers	Share
Semantic Segmentation	6	17.14%
Instance Segmentation	3	8.57%
Object Detection	3	8.57%
Image Dehazing	1	2.86%
Face Recognition	1	2.86%
Image Segmentation	1	2.86%
One-Shot Learning	1	2.86%
Scene Text Detection	1	2.86%
Text Detection	1	2.86%