  Scale variance among different sizes of body parts and objects is a
challenging problem for visual recognition tasks. Existing works usually design
dedicated backbone or apply Neural architecture Search(NAS) for each task to
tackle this challenge. However, existing works impose significant limitations
on the design or search space. To solve these problems, we present ScaleNAS, a
one-shot learning method for exploring scale-aware representations. ScaleNAS
solves multiple tasks at a time by searching multi-scale feature aggregation.
ScaleNAS adopts a flexible search space that allows an arbitrary number of
blocks and cross-scale feature fusions. To cope with the high search cost
incurred by the flexible space, ScaleNAS employs one-shot learning for
multi-scale supernet driven by grouped sampling and evolutionary search.
Without further retraining, ScaleNet can be directly deployed for different
visual recognition tasks with superior performance. We use ScaleNAS to create
high-resolution models for two different tasks, ScaleNet-P for human pose
estimation and ScaleNet-S for semantic segmentation. ScaleNet-P and ScaleNet-S
outperform existing manually crafted and NAS-based methods in both tasks. When
applying ScaleNet-P to bottom-up human pose estimation, it surpasses the
state-of-the-art HigherHRNet. In particular, ScaleNet-P4 achieves 71.6% AP on
COCO test-dev, achieving new state-of-the-art result.
