【PANet】《Path Aggregation Network for Instance Segmentation》
作者：互联网
CVPR-2018，Pytroch code
文章目录
1 Background and Motivation
作者发现 information propagation in state-of-the-art Mask R-CNN can be further improved
在 Mask R-CNN 基础上改进，进一步提升目标检测和实例分割的效果
2 Advantages / Contributions
提出 Path Aggregation Network（PANet） aiming at boosting information flow in proposal-based instance segmentation framework
- 1st place in the COCO 2017 Challenge Instance Segmentation task
- 2nd place in the COCO 2017 Challenge Object Detection task
- SOTA on MVD and Cityscapes
3 Method
三个改进模块
3.1 Bottom-up Path Augmentation
现有 FPN 结构的缺陷：
there is a long path from low-level structure to topmost features, increasing difficulty to access accurate localization information【图 1 （a）中红色虚箭头，前向传播时底层信息得经过整个 backbone 才能到达顶层，eg 到达 P5 层】
作者改进：
A bottom-up path is augmented to make low-layer information easier to propagate.【图 1 （a）中绿色虚箭头 】
细节如下：
Bottom-up Path 搭建方式是图 2 中的逆 FPN（自顶向下） 形式
注意 N 2 N_2 N2 is simply P 2 P_2 P2, without any processing
3.2 Adaptive Feature Pooling
缺陷：
熟悉 FPN 的小伙伴应该知道，proposals are assigned to different feature levels according to the size of proposals，像 “八爪鱼”，多条“腿”，一个 head，
two pro-posals with 10-pixel difference can be assigned to different levels，具体映射关系可以参考 Mask RCNN without Mask
information discarded in other levels may be helpful for final prediction
作者改进（每条腿上都接个头）：
We use max operation to fuse features from different levels
把同一 proposal 所有 level 的信息融合起来，而不是根据 proposal 的大小来决定采用 FPN 哪层 level 的特征
下面这个图就可以很直观的感受到利用多 level feature 的必要
横坐标是原 FPN 的 level，折线是采用 Adaptive Feature Pooling 之后的 level
以蓝色的 level1 折线为例，采用 Adaptive Feature Pooling 之后发现，属于 level1 范围大小的 proposal 仅用了 ~30% 的 level 1 特征，其余特征为 ~30% level 2， ~20% level3， ~20% level4（原 FPN 属于 level1 范围大小的 proposal 采用 100% level 1 特征）
可以看到 Adaptive Feature Pooling 使每个 proposal 的特征更加丰富！
3.3 Fully-connected Fusion
缺陷：
Mask R-CNN 方法中，mask prediction is made on a single view（卷积），losing the chance to gather more diverse information
作者的改进：
A complementary branch capturing different views——引入了平行的 FC 分支，最后与 conv 分支融合来预测 mask
作者认为 FC 的优势在于
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FC layers are location sensitive since predictions at different spatial locations are achieved by varying sets of parameters. So they have the ability to adapt to different spatial locations.
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Also prediction at each spatial location is made with global information of the entire proposal.
4 Experiments
4.1 Datasets
- COCO
- Cityscapes
- MVD
4.2 Experiments on COCO
1）Instance Segmentation Results
2）Object Detection Results
3）Component Ablation Studies
A
P
AP
AP 是分割任务的结果，
A
P
b
b
AP^{bb}
APbb 是单独训练目标检测的结果，
A
P
b
b
M
AP^{bbM}
APbbM 是联合训练目标检测和分割的结果
tricks 的效果提升占了 50%
Half of the improvement is from multi-scale training and multi-GPU sync. BN
4）Ablation Studies on Adaptive Feature Pooling
5）Ablation Studies on Fully-connected Fusion
6）COCO 2017 Challenge
引入更多的 trick
1st，DCN 是 Deformable convolutional networks
2nd
4.3 Experiments on Cityscapes
4.4 Experiments on MVD
标签：information,Segmentation,Network,level,PANet,Feature,Experiments,proposal,Adapti 来源： https://blog.csdn.net/bryant_meng/article/details/119382238