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Semantic-Segmentation-Resources

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Semantic Segmentation with CNN

Paper, Code, and Other Resources about Semantic Segmentation.

Semantic Image Segmentation

  1. Fully Convolutional Network for Semantic Segmentation. CVPR 2015, TPAM I2016 [Paper] [Code]
  2. DeepLab v1: Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. ICLR 2015
  3. CRFasRNN: Conditional Random Fields as Recurrent Neural Networks. ICCV 2015.
  4. ParseNet: Looking Wider to See Better. ICLR 2016 [Paper][Code]
  5. DeepLab v2: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. TPAMI 2017.
  6. DeepLab v3: Rethinking Atrous Convolution for Semantic Image Segmentation
  7. DeepLab v3+: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.
    • combine spatial pyramid pooling module and encoder-decoder structure.
    • the former one make full use of context info, the latter one get finer boundaries.
  8. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. TPAMI 2017.
  9. ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.
  10. RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation.
  11. PSPNet: Pyramid Scene Parsing Network.
  12. ICNet for Real-Time Semantic Segmentation on High-Resolution Images.
  13. LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
  14. UNet: Convolutional Networks for Biomedical Image Segmentation
  15. Not all pixels are equal
  16. Tiramisu The One Hundered Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
  17. spn: learning affinity from spatial propogation network. NIPS2017.
    • We propose spattial propagation networks for learning the affinity for vision tasks.
    • The model can learn semantically-aware affinity value for high-level vision tasks due to the powerful learning capability of deep CNNs.
    • Valide the framework on the task of refinement of image segmentation boundaries. Experiments show that the spatial propagation network provides a general, effictive and efficient solution for generating high-quality segmentation results.

Survey & Review

  1. A Review on Deep Learning Techniques Applied to Semantic Segmentation
  2. Real-time semantic segmentation comparative survey.

Semantic Video Segmentation

Specific Segmentation & Applications

  1. Portrait Segmentation for Image Stylization. EG2016.
  2. Deep Automatic Matting. CVPR2017.

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Semantic-Segmentation-Resources