  The architectural advancements in deep neural networks have led to remarkable
leap-forwards across a broad array of computer vision tasks. Instead of relying
on human expertise, neural architecture search (NAS) has emerged as a promising
avenue toward automating the design of architectures. While recent achievements
in image classification have suggested opportunities, the promises of NAS have
yet to be thoroughly assessed on more challenging tasks of semantic
segmentation. The main challenges of applying NAS to semantic segmentation
arise from two aspects: (i) high-resolution images to be processed; (ii)
additional requirement of real-time inference speed (i.e., real-time semantic
segmentation) for applications such as autonomous driving. To meet such
challenges, we propose a surrogate-assisted multi-objective method in this
paper. Through a series of customized prediction models, our method effectively
transforms the original NAS task into an ordinary multi-objective optimization
problem. Followed by a hierarchical pre-screening criterion for in-fill
selection, our method progressively achieves a set of efficient architectures
trading-off between segmentation accuracy and inference speed. Empirical
evaluations on three benchmark datasets together with an application using
Huawei Atlas 200 DK suggest that our method can identify architectures
significantly outperforming existing state-of-the-art architectures designed
both manually by human experts and automatically by other NAS methods.
