  Current NAS-based semantic segmentation methods focus on accuracy
improvements rather than light-weight design. In this paper, we proposed a
two-stage framework to design our NAS-based RSPNet model for light-weight
semantic segmentation. The first architecture search determines the inner cell
structure, and the second architecture search considers exponentially growing
paths to finalize the outer structure of the network. It was shown in the
literature that the fusion of high- and low-resolution feature maps produces
stronger representations. To find the expected macro structure without manual
design, we adopt a new path-attention mechanism to efficiently search for
suitable paths to fuse useful information for better segmentation. Our search
for repeatable micro-structures from cells leads to a superior network
architecture in semantic segmentation. In addition, we propose an RSP
(recursive Stage Partial) architecture to search a light-weight design for
NAS-based semantic segmentation. The proposed architecture is very efficient,
simple, and effective that both the macro- and micro- structure searches can be
completed in five days of computation on two V100 GPUs. The light-weight NAS
architecture with only 1/4 parameter size of SoTA architectures can achieve
SoTA performance on semantic segmentation on the Cityscapes dataset without
using any backbones.
