refineNet

发表于 |

RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

引用量1452，但是没有几篇技术博客？？

  1. 动机

    • 语义分割
      • dense classification on every single pixel
    • refineNet
      • long-range residual connections
      • chained residual pooling

  2. 论点

    • pooling/conv stride：
      • losing finer image structure
      • deconv is not able to recover the lost info
    • atrous
      • high reso：large computation
      • dilated conv：coarse sub-sampling of feature
    • FCN
      • fuse features from all levels
      • stage-wise rather than end-to-end???存疑

    • this paper

      • main idea：effectively exploit middle layer features
      • RefineNet
        • fuse all level feature
        • residual connections with identity skip
        • chained residual pooling to capture background context：看描述感觉像inception downsamp
        • end-to-end
        • 是整个分割网络中的一个component

  3. 方法

    • backbone
      • pretrained resnet
      • 4 blocks：x4 - x32，each block：pool-residual
      • connection：每个输出连接一个RefineNet unit
    • 4-cascaded architecture
      • final ouput：
        • high-resolution feature maps
        • dense soft-max
        • bilinear interpolation to origin resolution
      • cascade inputs
        • output from backbone block
        • ouput from previous refineNet block
    • refineNet block
      • adapt conv：
        • to adapt the dimensionality and refine special task
        • BN layers are removed
        • channel 512 for R4，channel 256 for the rest
      • fusion：
        • 先用conv to adapt dimension and recale the paths
        • 然后upsamp
        • summation
        • 如果single input：walk through and stay unchanged
      • chained residual pooling：
        • aims to capture background context from a large image region
        • chained：efficiently pool features with multiple window sizes
        • pooling blocks：s1 maxpooling+conv
        • in practice用了两个pooling blocks
        • use one ReLU in the chained residual pooling block
      • output conv：
        • 一个residual：to employ non-linearity
        • dimension remains unchanged
        • final level：two additional RCUs before the final softmax prediction
    • residual identity mappings
      • a clean information path not block by any non-linearity：所有relu都在residual path里面
      • 只有chained residual pooling模块起始时候有个ReLU：one single ReLU in each RefineNet block does not noticeably reduce the effectiveness of gradient flow
      • linear operations：
        • within the fusion block
        • dimension reduction operations
        • upsamp operations

  4. 其他结构

    • 级联的就叫cascaded
    • 一个block就叫single
    • 多个input resolution就叫mult-scale

  5. 实验

    • 4-cascaded works better than 1-cas & 2-cas
    • 2-scale works better than 1-scale