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Semantic segmentation networks adopt transfer learning from image classification networks causing 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<sup>2</sup>) which brings multi-scale scheme into fine-grained multi-scale object context using convolutions with meshgrid-like scattered dilation rates. SpaceMeshLab (ResNet-101 &#x002B; SpaM &#x002B; MetroCon<sup>2</sup>) achieves 82.0&#x0025; mIoU in Cityscapes test and 53.5&#x0025; mIoU on Pascal-Context val set.

SpaceMeshLab: Spatial Context Memoization And Meshgrid Atrous Convolution Consensus For Semantic Segmentation