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

Latest Papers

PAPER DATE
Pyramid Attention Networks for Image Restoration
| Yiqun MeiYuchen FanYulun ZhangJiahui YuYuqian ZhouDing LiuYun FuThomas S. HuangHumphrey Shi
2020-04-28
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
| Chien-Yao WangHong-Yuan Mark LiaoI-Hau YehYueh-Hua WuPing-Yang ChenJun-Wei Hsieh
2019-11-27
PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment
| Kaixin WangJun Hao LiewYingtian ZouDaquan ZhouJiashi Feng
2019-08-18
iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
Syed Waqas ZamirAditya AroraAkshita GuptaSalman KhanGuolei SunFahad Shahbaz KhanFan ZhuLing ShaoGui-Song XiaXiang Bai
2019-05-30
Path Aggregation Network for Instance Segmentation
| Shu LiuLu QiHaifang QinJianping ShiJiaya Jia
2018-03-05

Tasks

TASK PAPERS SHARE
Object Detection 3 18.75%
Semantic Segmentation 3 18.75%
Instance Segmentation 2 12.50%
Demosaicking 1 6.25%
Denoising 1 6.25%
Image Denoising 1 6.25%
Image Restoration 1 6.25%
Super-Resolution 1 6.25%
Image Classification 1 6.25%

Categories