Bottom-up Path Augmentation

Introduced by Liu et al. in Path Aggregation Network for Instance Segmentation

Bottom-up Path Augmentation is a feature extraction technique that seeks to shorten the information path and enhance a feature pyramid with accurate localization signals existing in low-levels. This is based on the fact that high response to edges or instance parts is a strong indicator to accurately localize instances.

Each building block takes a higher resolution feature map $N_{i}$ and a coarser map $P_{i+1}$ through lateral connection and generates the new feature map $N_{i+1}$ Each feature map $N_{i}$ first goes through a $3 \times 3$ convolutional layer with stride $2$ to reduce the spatial size. Then each element of feature map $P_{i+1}$ and the down-sampled map are added through lateral connection. The fused feature map is then processed by another $3 \times 3$ convolutional layer to generate $N_{i+1}$ for following sub-networks. This is an iterative process and terminates after approaching $P_{5}$. In these building blocks, we consistently use channel 256 of feature maps. The feature grid for each proposal is then pooled from new feature maps, i.e., {$N_{2}$, $N_{3}$, $N_{4}$, $N_{5}$}.

Source: Path Aggregation Network for Instance Segmentation

Latest Papers

PAPER DATE
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| Chien-Yao WangHong-Yuan Mark LiaoI-Hau YehYueh-Hua WuPing-Yang ChenJun-Wei Hsieh
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PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment
| Kaixin WangJun Hao LiewYingtian ZouDaquan ZhouJiashi Feng
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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
Hybrid Task Cascade for Instance Segmentation
| Kai ChenJiangmiao PangJiaqi WangYu XiongXiaoxiao LiShuyang SunWansen FengZiwei LiuJianping ShiWanli OuyangChen Change LoyDahua Lin
2019-01-22
Path Aggregation Network for Instance Segmentation
| Shu LiuLu QiHaifang QinJianping ShiJiaya Jia
2018-03-05

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