The way that information propagates in neural networks is of great
importance. In this paper, we propose Path Aggregation Network (PANet) aiming
at boosting information flow in proposal-based instance segmentation framework.
Specifically, we enhance the entire feature hierarchy with accurate
localization signals in lower layers by bottom-up path augmentation, which
shortens the information path between lower layers and topmost feature. We
present adaptive feature pooling, 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. These
improvements are simple to implement, with subtle extra computational overhead.
Our PANet reaches the 1st place in the COCO 2017 Challenge Instance
Segmentation task and the 2nd place in Object Detection task without
large-batch training. It is also state-of-the-art on MVD and Cityscapes. Code
is available at https://github.com/ShuLiu1993/PANet