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Cheng-You Lu

Deeplab

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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs Author: Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille Publish: arXiv Date: 12 May 2017
Architecture (Map size:x4) (Map size:x8) (Map size:%32)
DCNN  VGG-16 or ResNet-101  Atrous convolution Keep Spatial Resolution Number of filter parameters stay constant Number of operations per position stay constant  Atrous Spatial Pyramid Pooling (ASPP) Multiscale Image Representations
Atrous convolution
ASPP
Other Method
Fully Connected CRF  Deeper models with multiple maxpooling Good for classification Increase invariance ->Rough position of objects  Dense CRF Machine learning method Speed up by high-dimensional filtering algorithm
Dense CRF Unary potential : Energy function : CRF can be characterized by Gibbs distribution: : the label assignment probability at pixel I as computed by a DCNN
Pairwise potential appearance kernel smoothness kernel Label compatibility function: = 1 for Xi = Xj 0 otherwise Pixel position: p Pixel color value: I
Example High penalty Low penalty
Example
Result
Result
Reference  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs  Rethinking Atrous Convolution for Semantic Image Segmentation  Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials

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Deeplab

  • 1. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs Author: Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille Publish: arXiv Date: 12 May 2017
  • 3. DCNN  VGG-16 or ResNet-101  Atrous convolution Keep Spatial Resolution Number of filter parameters stay constant Number of operations per position stay constant  Atrous Spatial Pyramid Pooling (ASPP) Multiscale Image Representations
  • 7. Fully Connected CRF  Deeper models with multiple maxpooling Good for classification Increase invariance ->Rough position of objects  Dense CRF Machine learning method Speed up by high-dimensional filtering algorithm
  • 8. Dense CRF Unary potential : Energy function : CRF can be characterized by Gibbs distribution: : the label assignment probability at pixel I as computed by a DCNN
  • 9. Pairwise potential appearance kernel smoothness kernel Label compatibility function: = 1 for Xi = Xj 0 otherwise Pixel position: p Pixel color value: I
  • 14. Reference  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs  Rethinking Atrous Convolution for Semantic Image Segmentation  Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials