  High-resolution representations (HR) are essential for dense prediction tasks
such as segmentation, detection, and pose estimation. Learning HR
representations is typically ignored in previous Neural Architecture Search
(NAS) methods that focus on image classification. This work proposes a novel
NAS method, called HR-NAS, which is able to find efficient and accurate
networks for different tasks, by effectively encoding multiscale contextual
information while maintaining high-resolution representations. In HR-NAS, we
renovate the NAS search space as well as its searching strategy. To better
encode multiscale image contexts in the search space of HR-NAS, we first
carefully design a lightweight transformer, whose computational complexity can
be dynamically changed with respect to different objective functions and
computation budgets. To maintain high-resolution representations of the learned
networks, HR-NAS adopts a multi-branch architecture that provides convolutional
encoding of multiple feature resolutions, inspired by HRNet. Last, we proposed
an efficient fine-grained search strategy to train HR-NAS, which effectively
explores the search space, and finds optimal architectures given various tasks
and computation resources. HR-NAS is capable of achieving state-of-the-art
trade-offs between performance and FLOPs for three dense prediction tasks and
an image classification task, given only small computational budgets. For
example, HR-NAS surpasses SqueezeNAS that is specially designed for semantic
segmentation while improving efficiency by 45.9%. Code is available at
https://github.com/dingmyu/HR-NAS
