RFB

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RFB: Receptive Field Block Net for Accurate and Fast Object Detection

  1. 动机

    • RF block：Receptive Fields
    • strengthen the lightweight features using a hand-crafted mechanism：轻量，特征表达能力强
    • assemble RFB to the top of SSD

  2. 论点

    • lightweight

      • enhance feature representation

    • 人类

      • 群智感受野（pRF）的大小是其视网膜图中偏心率的函数
      • 感受野随着偏心率而增加
      • 更靠近中心的区域在识别物体时拥有更高的比重或作用

      • 大脑在对于小的空间变化不敏感

    • fixed sampling grid (conv)

      • probably induces some loss in the feature discriminability as well as robustness

    • inception

      • RFs of multiple sizes
      • but at the same center

    • ASPP

      • with different atrous rates
      • the resulting feature tends to be less distinctive

    • Deformable CNN

      • sampling grid is flexible

      • but all pixels in an RF contribute equally

    • RFB

      • varying kernel sizes
      • applies dilated convolution layers to control their eccentricities
      • 组合来模拟human visual system
      • concat

      • 1x1 conv for fusion

    • main contributions

      • RFB module: enhance deep features of lightweight CNN networks
      • RFB Net: gain on SSD
      • assemble on MobileNet

  3. 方法

    • Receptive Field Block

      • 类似inception的multi-branch

      • dilated pooling or convolution layer

    • RFB Net

      • SSD-base

      • 头上有较大分辨率的特征图的conv层are replaced by the RFB module

      • 特别头上的conv层就保留了，因为their feature maps are too small to apply filters with large kernels like 5 × 5

      • stride2 module：每个conv stride2，那id path得变成1x1 conv？