SegNet is a semantic segmentation model. This core trainable segmentation architecture consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network. The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature maps. Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling.
A Pyramid Pooling Module is a module for semantic segmentation which acts as an effective global contextual prior. The motivation is that the problem of using a convolutional network like a ResNet is that, while the receptive field is already larger than the input image, the empirical receptive field is much smaller than the theoretical one especially on high-level layers. This makes many networks not sufficiently incorporate the momentous global scenery prior. The PPM is an effective global prior representation that addresses this problem. It contains information with different scales and varying among different sub-regions. Using our 4-level pyramid, the pooling kernels cover the whole, half of, and small portions of the image. They are fused as the global prior. Then we concatenate the prior with the original feature map in the final part.
PSPNet, or Pyramid Scene Parsing Network, is a semantic segmentation model that utilises a pyramid parsing module that exploits global context information by different-region based context aggregation. The local and global clues together make the final prediction more reliable. We also propose an optimization Given an input image, PSPNet use a pretrained CNN with the dilated network strategy to extract the feature map. The final feature map size is $1/8$ of the input image. On top of the map, we use the pyramid pooling module to gather context information. Using our 4-level pyramid, the pooling kernels cover the whole, half of, and small portions of the image. They are fused as the global prior. Then we concatenate the prior with the original feature map in the final part of. It is followed by a convolution layer to generate the final prediction map.