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.
PANet REF aims to improve the information propagation of Mask R-CNN by introducing bottom-up path augmentation, adaptive feature pooling, and fully-connected fusion.
Path Aggregation Network for Instance Segmentation
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Computer Science