The document presents the Path Aggregation Network (PANet) for boosting information flow in instance segmentation. PANet enhances the feature hierarchy with accurate localization signals, introduces adaptive feature pooling, and a complementary branch for improved mask prediction. The paper discusses the importance of information propagation in neural networks and details the design principles of PANet. Experimental results show PANet's state-of-the-art performance on various datasets and tasks. The approach is shared by object detection and instance segmentation, leading to enhanced performance in both areas.