  This paper exploits the intrinsic features of urban-scene images and proposes
a general add-on module, called height-driven attention networks (HANet), for
improving semantic segmentation for urban-scene images. It emphasizes
informative features or classes selectively according to the vertical position
of a pixel. The pixel-wise class distributions are significantly different from
each other among horizontally segmented sections in the urban-scene images.
Likewise, urban-scene images have their own distinct characteristics, but most
semantic segmentation networks do not reflect such unique attributes in the
architecture. The proposed network architecture incorporates the capability
exploiting the attributes to handle the urban scene dataset effectively. We
validate the consistent performance (mIoU) increase of various semantic
segmentation models on two datasets when HANet is adopted. This extensive
quantitative analysis demonstrates that adding our module to existing models is
easy and cost-effective. Our method achieves a new state-of-the-art performance
on the Cityscapes benchmark with a large margin among ResNet-101 based
segmentation models. Also, we show that the proposed model is coherent with the
facts observed in the urban scene by visualizing and interpreting the attention
map. Our code and trained models are publicly available at
https://github.com/shachoi/HANet
