Large receptive field could exploit more information from an input image. To achieve high performance, recent works have expanded the size of receptive field. Attention has been mainly used to get large receptive field. However, the computation of attention costs extremely expensive. In this paper, we attempt to resolve this problem in other way which covers large receptive field. First, we exploit properties of layer/instance normalization methods. This optimizes parameters and features, reducing additional computational cost. In addition, we analyze low performance on small objects with vertical axis and propose vertical self-attention by adopting pooling with vertical direction on query and key. We achieve the mean Interaction-of-union(mIoU) of 73.1 and the frame per second(fps) of 191, which are comparable results with state-of-the-arts on Cityscapes test datasets.