Abstract: Semantic segmentation networks adopt transfer learning from image
classification networks which occurs a shortage of spatial context information.
For this reason, we propose Spatial Context Memoization (SpaM), a bypassing
branch for spatial context by retaining the input dimension and constantly
communicating its spatial context and rich semantic information mutually with
the backbone network. Multi-scale context information for semantic segmentation
is crucial for dealing with diverse sizes and shapes of target objects in the
given scene. Conventional multi-scale context scheme adopts multiple effective
receptive fields by multiple dilation rates or pooling operations, but often
suffer from misalignment problem with respect to the target pixel. To this end,
we propose Meshgrid Atrous Convolution Consensus (MetroCon^2) which brings
multi-scale scheme into fine-grained multi-scale object context using
convolutions with meshgrid-like scattered dilation rates. SpaceMeshLab
(ResNet-101 + SpaM + MetroCon^2) achieves 82.0% mIoU in Cityscapes test and
53.5% mIoU on Pascal-Context validation set.