  Scale-permuted networks have shown promising results on object bounding box
detection and instance segmentation. Scale permutation and cross-scale fusion
of features enable the network to capture multi-scale semantics while
preserving spatial resolution. In this work, we evaluate this meta-architecture
design on semantic segmentation - another vision task that benefits from high
spatial resolution and multi-scale feature fusion at different network stages.
By further leveraging dilated convolution operations, we propose SpineNet-Seg,
a network discovered by NAS that is searched from the DeepLabv3 system.
SpineNet-Seg is designed with a better scale-permuted network topology with
customized dilation ratios per block on a semantic segmentation task.
SpineNet-Seg models outperform the DeepLabv3/v3+ baselines at all model scales
on multiple popular benchmarks in speed and accuracy. In particular, our
SpineNet-S143+ model achieves the new state-of-the-art on the popular
Cityscapes benchmark at 83.04% mIoU and attained strong performance on the
PASCAL VOC2012 benchmark at 85.56% mIoU. SpineNet-Seg models also show
promising results on a challenging Street View segmentation dataset. Code and
checkpoints will be open-sourced.
