Abstract
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 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. Yet they are useful and make
our PANet reach the 1
st place in the COCO 2017 Challenge Instance Segmentation task and the 2
nd place in Object Detection task without large-batch training. PANet is
also state-of-the-art on MVD and Cityscapes