PANet

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PANet: Path Aggregation Network for Instance Segmentation

  1. 动机

    • boost the information flow
    • bottom-up path
      • shorten information path
      • enhance accurate localization
    • adaptive feature pooling
      • aggregate all levels
      • avoiding arbitrarily assigned results
    • mask prediction head
      • fcn + fc
      • captures different views, possess complementary properties
    • subtle extra computational

  2. 论点

    • previous skills: fcn, fpn, residual, dense
    • findings
      • 高层特征类别准，底层特征定位准，但是高层和底层特征之间的path太长了，不利于双高
      • past proposals make predictions based on one level
    • PANet
      • bottom-up path
        • shorten information path
        • enhance accurate localization
      • adaptive feature pooling
        • aggregate all levels
        • avoiding arbitrarily assigned results
      • mask prediction head
        • fcn + fc
        • captures different views, possess complementary properties

  3. 方法

    • framework

      • b: bottom-up path
      • c: adaptive feature pooling
      • e: fusion mask branch

    • bottom-up path

      • fpn’s top-down path:
        • to propagate strong semantical information
        • to ensure reasonable classification capability
        • long path: red line, 100+ layers
      • bottom-up path:
        • enhances the localization capability
        • short path: green line, less than 10 layers

      • for each level $N_l$

        • input: $N_{l+1}$ & $P_l$
        • $N_{l+1}$ 3x3 conv & $P_l$ id path - add - 3x3 conv
        • channel 256
        • ReLU after conv

    • adaptive feature pooling

      • pool features from all levels, then fuse, then predict

      • steps

        • map each proposal to all feature levels
        • roi align
        • go through one layer of the following sub-networks independently
        • fusion operation (element-wise max or sum)

        • 例如，box branch是两个fc层，来自各个level的roi align之后的proposal features，先各自经过一个fc层，再share the following till the head，mask branch是4个conv层，来自各个level的roi align之后的proposal features，先各自经过一个conv层，再share the following till the head

      • fusion mask branch

        • fc layers are location sensitive
        • helpful to differentiate instances and recognize separate parts belonging to the same object
        • conv分支
          • 4个连续conv+1个deconv：3x3 conv，channel256，deconv factor=2
          • predict mask of each class：output channel n_classes
        • fc分支
          • from conv分支的conv3输出
          • 2个连续conv，channel256，channel128
          • fc，dim=28x28，特征图尺寸，用于前背景分类

        • final mask：add

  4. 实验

    • heavier head
      • 4 consecutive 3x3 convs
      • shared among reg & cls
      • 在multi-task的情况下，对box的预测有效